case study on water management in india

G20 Compendium on Best Practices for Water Management

Ministry of Jal Shakti and Council on Energy, Environment and Water July 2023 | Sustainable Water

Suggested citation:  MoJS and CEEW. 2023. Best Practices for Water Management . Ministry of Jal Shakti, New Delhi, India

This compendium was developed through a synthesis of cases of best practices for water management in the G20 countries. For this purpose, the Ministry of Jal Shakti, Government of India developed a best practice template and shared with the G20 member countries to report programmes and interventions undertaken by them, across but not limited to the following thematic areas: universalisation of water and sanitation services; participatory groundwater management; climate resilient water infrastructure; water use efficiency improvement; and any other water management programmes or interventions that have led to improved water governance, better data and information, models for financing water infrastructure, and capacity building of stakeholders.

Key Highlights

• The compendium on ‘Best Practices for Water Management’ includes 40 innovative case studies on best practices for water management received from the G20 member countries.
• The case studies cover an array of themes including water use efficiency, river rejuvenation, climate resilient infrastructure , safe drinking water , hydro energy management, water data and information, flood management, drought management, water body restoration, global knowledge partnerships on water management, water harvesting, water supply augmentation, efficient water governance, wastewater management , watershed management and civil society participation, and groundwater management.
• The upcoming G20 Presidencies may take this journey to a new level by collating thematic grouping of best practices for specific areas of water resources such as drinking water and sanitation, flood management, irrigation management, basin planning, water quality etc.

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Groundwater governance in India – A case study by World Bank

It examines the impediments to better governance of groundwater, and explores opportunities for using groundwater to help developing countries adapt to climate change. It attempts to understand the practical issues that arise in establishing robust national governance frameworks for groundwater and in implementing these frameworks at the aquifer level.

The case study focused on the national, state and local levels. At the national and state levels, it analyzed the policy, legal, and institutional arrangements to identify the demand and supply management and incentive structures that have been established for groundwater management. At the local level, it assessed the operations, successes, and constraints facing local institutions in the governance of a number of aquifers within peninsula India, on the coast and on the plain of the Ganges river valley.

The report is divided into eight chapters following which a list of references used in the paper is used is provided. The first chapter in the beginning provides a brief background to the study and defines “groundwater governance”. In this report it refers to “refers to those political, social, economic, and administrative systems that are explicitly aimed at developing and managing water resources and water services at different levels of society that rely solely or largely on groundwater resources”. Following this the methodology used to carry out the study is elaborated where emphasis on pragmatic approaches, which can bring is incremental improvements with the given institutional framework is highlighted. The study is based on:

• the findings and recommendations of  “Deep Wells and Prudence: Towards Pragmatic Action for Addressing Groundwater Overexploitation in India” ,which focused mainly on aquifer intensive abstraction groundwater issues (World Bank 2010).
• number of  Groundwater Management Advisory Team (GW-MATE) case profile and strategic overview series publications, which addressed in more detail the local level in seven rural and urban aquifers; and
• reports on groundwater quality-related aspects prepared by two local consultants aimed at addressing the technical/managerial and legal/institutional dimensions of aquifer protection in the country.

 Chapter 3 is on “The Governance Framework”. With a brief over view of key aspects related to ground water and its lacunas in the national water policy of 1987 and 2002 the report points at ground water in the Indian legal system and policy framework. Following which the institutions that govern the development and management of ground water is elaborated. This section covers the following issues: quality protection and pollution of ground water, its monitoring and surveillance the institutional capacity of institutions and financial issues.

 Chapter 4 is on “Case Study Aquifers/Pilot Projects”. To cover the diverse rural and urban environments with different socioeconomic features seven cases of aquifers had been selected for this study. The chapter discusses in detail about these cases.

 Chapter 5 is on “Findings and Lessons Learned”. It states that technical, legal, and institutional provisions are in a more or less acceptable. As far as the implementation of actions proposed by GWMATE is also uncertain as the institutional capacity is weak. The chapter then lays down a list of lessons learned about intensive groundwater use in hardrock peninsular India and alluvial Indo Gangetic Plain. It also highlights on the issue of coping with groundwater pollution issues.

 Chapter 6 is on “Groundwater Governance and Climate Change Adaptation”. It gives a brief description (conjunctive use and recharge enhancement) of the World Bank’s study on ground water and climate change in cases where GW-MATE has been involved.

Chapter 7 is on “Recommendations”. A summary of: recommended implementation actions for managing intensive groundwater abstraction and actions required for protecting ground water pollution is given in this chapter. Further it also highlights at the actions required to strengthen state groundwater development and management agencies.

 Chapter 8 provides list annexes of the report.

 Click below to download the report.

Helping India Overcome Its Water Woes

Urban Water supply in Belgaum District, Karnataka

Dipankar Ghoshal/World Bank

What are the challenges that India faces with water management, especially given that we often have too little or too much water?

India is home to 18 percent of the global population but has only 4 percent of the global water resources. Its per capita water availability is around 1,100 cubic meter (m3), well below the internationally recognized threshold of water stress of 1,700 m3 per person, and dangerously close to the threshold for water scarcity of 1,000 m3 per person.

Population growth and economic development put further pressure on water resources. Climate change is expected to increase variability and to bring more extreme weather events.

Paradoxically, India is also the largest net exporter of virtual water (the amount of water required to produce the products that India exports) and has one of the most water-intense economies. Despite looming water scarcity, India is one of the largest water users per unit of gross domestic product (GDP). This suggests that the way in which India manages its scarce water resources accounts for much of its water woes.

Government capacities are lacking as far as improving water management is concerned, while policies and incentives often favor inefficient and unproductive use of water. This is coupled with weak or absent institutions (e.g. for water regulation) and poor data collection and assessment.

What important lessons in water management can India learn from other countries?

We don’t have to go overseas to see good examples of water resources management. The Maharashtra Water Resources Regulatory Authority , established under a World Bank project, is putting in place policies, regulations, institutions and incentives that promote more efficient and more productive use of water, e.g., by ensuring the equitable distribution of water among users, and by establishing water tariffs.

Efforts to establish effective authorities are also underway in other states, and Maharashtra is disseminating the lessons learned from its experience.

In India, experience with improving water service delivery has been mixed as, only in rare cases, have efforts been embedded in a favorable policy and regulatory environment. When it comes to improving water service delivery, India can learn from Brazil, Colombia, Mozambique and New South Wales (Australia), among others.

Poor or absent water management policies also exacerbate the effects of climate change on water. On the other hand, sound water management can neutralize many of the water-related impacts of climate change. Vietnam, for instance, has implemented a comprehensive program to manage water-related risks and build resilience. Nigeria has helped prevent erosion, reclaim valuable land and focused on sustainable livelihoods to reduce the vulnerability of people, infrastructure, assets, natural capital, and livelihoods to land degradation. And the Philippines is implementing comprehensive urban drainage works to improve water management.

How is the World Bank supporting this issue?

The World Bank’s Country Partnership Framework for India recognizes the importance of the efficient use of natural resources, including water, in support of the country’s ambitious growth targets. Several World Bank projects support India’s efforts in the water sector:

Through the National Mission for Clean Ganga , the World Bank is helping the Government of India build institutional capacity for the management and clean-up of the Ganga and investing to reduce pollution. The $1-billion operation has financed investments in wastewater and effluent treatment, solid waste management and river front development.

Another World Bank project, the Dam Rehabilitation and Improvement Project , has improved the safety and performance of 223 dams in the country through rehabilitation, capacity-strengthening and measures to enhance legal and institutional frameworks.

The National Hydrology Project is providing significant support to strengthen capacities, improve data monitoring and analysis, and laying the foundations for benchmarking and performance-based water management.

The Shimla Water Supply and Sewerage Service Delivery Reform Development Policy Loan supports the Government of Himachal Pradesh in its policy and institutional development program for improving water supply and sewerage services that are financially sustainable and managed by an accountable institution responsive to its customers.

The West Bengal Accelerated Development of Minor Irrigation supports farmer-led irrigation by improving service delivery to farming communities and linking these to agricultural markets.

Innovative instruments are being deployed to finance these operations, such as the development policy loan in Shimla, the program-for-results financing in the Swachh Bharat Mission Support Operation and the National Groundwater Management and Improvement Project , and the use of disbursement-linked indicators in Dam Rehabilitation and Improvement Project-II.

Analytical work at the World Bank focuses, among others, on irrigation and water and sanitation service delivery. The results will be incorporated into future lending operations.

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Climate Resilience in Water Resource Management in India: A Conceptual Framework for Action

The water sector in India is facing increasing variability and unpredictability of water resources due to climate change. This is compounded by inadequate infrastructure for water storage and distribution, and the insufficient integration of climate resilience into water management policies. This is highlighted by the IPCC's Sixth Assessment Report. Key threats include extreme weather events, rising temperatures, erratic monsoons, and sea level rise, impacting agriculture, industry, ecosystems, and overall water security.

To build a sustainable future, climate-resilient water management strategies are imperative. These include integrated water resources management (IWRM), nature-based solutions (NbS), and advanced technologies further aided by policies and laws. Both adaptation and mitigation strategies are necessary: adaptation builds resilience against immediate impacts, while mitigation contributes to long-term sustainability by reducing greenhouse gas (GHG) emissions. Establishing robust systems for the continuous monitoring of water resources and climate data by upgrading meteorological and hydrological stations and utilizing advanced technologies such as remote sensing and GIS for better data accuracy would be vital for resilient water infrastructure. It is vital to strengthen institutional frameworks by ensuring that water management policies incorporate climate resilience principles by revising existing policies, enhancing coordination between different governmental agencies, and involving local communities in decision-making processes.

By embracing innovative solutions, fostering partnerships, updating regulations, laws, improving administration and encouraging community participation, India can build a climate-resilient water future. This approach will help ensure water security and facilitate the achievement of the Sustainable Development Goal (SDG)-6 (Clean Water and Sanitation) and several aligned SDGs.

Implementation of IWRM principles would foster collaboration among various sectors, including agriculture, industry, and urban development, to create a cohesive water management strategy that optimizes resource use and reduces conflict. Enhancing data collection and monitoring systems by deploying advanced technologies such as remote sensing, GIS, and IoT-based sensors to improve the accuracy and reliability of water and climate data. These could be presented on centralized databases and platforms for real-time data sharing among stakeholders, ensuring informed decision-making processes. This would help to the development of comprehensive strategies that address both local and national water challenges towards water security in India.

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Based on 20 detailed successful case studies from across India, this book outlines future rural water supply approaches for all lower-income countries following India's growth path. Google scholar

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Water crisis: a case study of Jabalpur

Watershed development in India: Case study summary

In 2014, PROFOR supported a study that aimed to gather lessons learned and good practices from three high profile and successful watershed management projects in India: The Karnataka Watershed Development Project, the Uttaranchal Decentralized Watershed Development Project, and the Himachal Pradesh Mid-Himalayan Watershed Development Project.

The main knowledge product was a peer-reviewed high-quality report that outlined the evolution of watershed development policy and practice in India.  The report consolidated lessons learned from best practices and contributes to improved policies and programs for watershed development and management. Apart from dissemination of the report, presentations were made at formal launch events, seminars and workshops in India and in Washington DC.

The most tangible outcomes of the report included new studies, projects and influence on project design within India and beyond to Nigeria, Malawi, and Haiti. This came about directly because decision-makers (or those who could influence decision-makers) had access to the findings of the study.

In India, the recommendations in the report heavily influenced the objectives of the Neeranchal National Watershed Project. In addition, the rationale for the “Catchment Assessment and Planning for Watershed Management” study, comes directly from the discussion in the report of managing upstream and downstream inter-relations.

In Nigeria and Malawi, senior Bank staff used material from the report to design the Nigeria Erosion and Watershed Management Project (NEWMAP) and the Shire River Basin Management Project respectively. The PROFOR work provides a good benchmark to compare the evolution of the Malawi and Nigeria watershed components during implementation.   In Haiti, following the formal launch of the report, the World Bank Task Team Leader (TTL) in charge of the HT Sustainable Rural and Small Towns Water and Sanitation Project contacted one of the report authors to discuss how the lessons learned could improve the design of the Haiti project.

The India, Malawi, Nigeria, and Haiti examples illustrate the nature of changes in World Bank practice in designing projects, initiating studies and re-aligning implementation processes as a result of the findings of the report. The India work, in particular, is significant in that, from the assorted work done by various agencies and programs over the years, the best practices have been condensed into revised guidelines for the national watershed program, the IWMP; and technical support has been provided to the main national-level watershed development program in India, the Prime Minister’s Krishi Sinchayee Yojana (PMKSY).

Given the size of the IWMP (~USD 500 million/year)  and, given that the IWMP is now the watershed component of an even larger nation-wide program, the PMKSY, with an outlay of USD 850 million for 2016-17 alone,  its potential impact is very large.

Over the course of the 8-year, USD 357 million Neeranchal project, this technical support is expected to translate into policy and program improvements that will affect the Indian watershed management program, for which the Government of India has allocated around USD 240 million for 2017-18 alone. [1] Given its objectives to address water resource and watershed management in dryland areas through improved technology and techniques, the IWMP could have significant impacts on poverty reduction, biodiversity conservation, and climate change.

[1] DoLR. 2016. Outcome Budget 2016-17 of Ministry of Rural Development, Department of Land Resources. [pdf] New Delhi: Department of Land Resources, Ministry of Rural Development, Government of India. Available at http://dolr.nic.in/dolr/downloads/pdfs/Outcome%20Budget%202016-17.pdf

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Open Access

Peer-reviewed

Research Article

An ecological framework to index crop yields using productivity and Ecosystem Fit: A case study from India

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, United States of America, Oak Ridge Institute for Science and Education (ORISE), Oakridge Associated Universities, Tennessee, United States of America

Roles Conceptualization, Methodology, Writing – review & editing

Affiliation School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, United States of America

Roles Conceptualization, Methodology, Writing – original draft, Writing – review & editing

Roles Conceptualization, Writing – review & editing

Affiliation Department of Civil and Environmental Engineering, University of Washington; Seattle, Washington, United States of America

Roles Conceptualization, Methodology, Visualization, Writing – review & editing

Affiliation Yale Pinchot Professor of Forestry and Environmental Studies Emeritus, 1500 SW 11th Ave. Unit 2304, Portland, Oregon, United States of America

• Angela M. Klock, 
• Amita Banerjee, 
• Kristiina A. Vogt, 
• Korena K. Mafune, 
• Daniel J. Vogt, 
• John C. Gordon

• Published: September 16, 2024
• https://doi.org/10.1371/journal.pstr.0000122
• Reader Comments

On the global scale, agricultural crop yields have decreased or plateaued over the last several decades. This suggests that the current focus on selecting crop varieties based on a plant’s light-use efficiency (photosynthetic and nitrogen-use-efficiency metrics) may not be sensitive to the site’s edaphic parameters, which limit growth. This study introduces a new framework to determine if crops can achieve higher yield potentials by assessing how plants adapt to the edaphic properties that impact growth, especially when contending with climate change. The new approach calculates an Ecosystem Fit index using a ratio of remotely sensed (or observed) total net primary productivity to the theoretical maximum productivity of the site. Then, it uses that index as a benchmark to judge quantitatively whether any new crop species or variety is improving potential biomass or economic yields at that specific site. It can also determine the best soil types for those crop varieties and monitor their potential adaptability relative to climate change over time. This study used a database of 356 spatially independent reference sites to develop this framework using a landcover classification of crops across 21 ecoregions and five biomes in India. It includes total net primary productivity data, theoretical maximum productivity potential, and soil and climatic data. This comparison showed that the light-use efficiency model, as intended, was not sensitive to variations in soil characteristics, temperature, or precipitation. Our framework showed significant differences in growth by soil type and precipitation and three significant productivity thresholds by soil type. The results of this study demonstrate that total crop productivity and Ecosystem Fit create a useful index for local land managers to assess growth and yield potentials across diverse edaphic landscapes and for decision-making with changing climates.

Author summary

Intensive farming practices emerged ~5,000 years ago to feed a growing human population. In the 1950s, the Green Revolution introduced fossil-fuel derived nitrogen fertilizers and pesticides to increase yields. Subsequently, plant photosynthetic- and nitrogen-use-efficiency models were used to assess yield potentials of crops and which varieties to plant across a diversity of agricultural landscapes. These contributed to globalizing agricultural productivity as yields increased but shifted crop selection to mainly utilizing nitrogen levels in the harvested product as the selection criteria. However, yields began to plateau or decrease globally in the 1990s, especially in developing countries. Also, a smaller percent of applied fertilizer nitrogen was found in the harvested product, while surplus nitrogen accumulated in the soil and polluted water systems via runoff or leaching. Using 356 site-level soil and climatic data from India, this study demonstrates that productivity measures allow managers to determine which management options increase the Ecosystem Fit of a crop given the site’s growth-limiting conditions. This shifts management options to optimizing total plant productivity based on local-edaphic and environmental growth-limiting factors. Thorough understanding of tradeoffs and feedbacks in crop ecophysiology will potentially help to reduce negative environmental externalities associated with agricultural production at the site level.

Citation: Klock AM, Banerjee A, Vogt KA, Mafune KK, Vogt DJ, Gordon JC (2024) An ecological framework to index crop yields using productivity and Ecosystem Fit: A case study from India. PLOS Sustain Transform 3(9): e0000122. https://doi.org/10.1371/journal.pstr.0000122

Editor: Semra Benzer, Gazi Universitesi, TÜRKIYE

Received: December 19, 2023; Accepted: July 24, 2024; Published: September 16, 2024

Copyright: © 2024 Klock et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data are in the manuscript and/or Supporting information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Efficient agricultural management and sustainable practices are needed to meet national food security demands. Today, more than 700 million people across the globe are estimated to be living in hunger [ 1 ] due in part to limited access to adequate amounts of food. Food insecurity at certain times throughout the year, or an inability to acquire adequate food due to lack of money or resources, is often considered one of the major causes of inadequate food production. This looming issue is further exacerbated by the increasing food and energy demands of a rapidly growing global population. Current estimates suggest the human population will increase from 7.6 billion to 9.7 billion by 2050 [ 2 ], and such an increase would require a 70% to 100% increase in the yield of major cash and commodity crops [ 2 – 5 ].

Expanding the land area in agricultural production is not a viable option to increase food production since the reserves of arable land are finite. Today, ~44.3% of the global habitable land area is already in agricultural use, 10.4% to grow crops, and 34.9% is designated as grazing land for animals or to produce animal feed [ 6 ]. The remaining 45.7% of land area is less suitable for farming because of poor soil quality, e.g., low soil organic matter levels, low water holding capacity, or low nutrient levels. Another example of why it may be hard to increase agricultural production in the United States is that Lark et al. [ 7 ] reported that 69.5% of new croplands in the United States already meet the average forecasted yield production. Further, current agricultural practices have polluted agrarian lands and left a legacy of damaged non-arable lands [ 8 – 9 ] with decreased biodiversity and loss of ecosystem services [ 4 , 10 – 12 ]. Therefore, converting previously farmed fields or non-arable lands into agricultural production would further contribute to soil and atmospheric pollution without appreciable gains in crop yields [ 8 ].

Another issue facing agriculture is that major food crops have reached the biological limit of increasing growth rates, as demonstrated by yields plateauing under current intensive agricultural management practices despite technological innovations in crop management [ 13 – 14 ]. Ray et al. [ 15 ] analyzed ~2.5 million global observations between 1961 and 2008 and found that yields increased in some areas but stagnated or collapsed in 24 to 39% of maize, rice, wheat, and soybean-growing regions. Similarly, Ritchie et al. [ 6 ] summarized global crop yields between 1961 and 2020, showing most have plateaued over the last decade, especially grain crops.

Historically, increasing yields resulted from management practices that optimized photosynthetic efficiencies and the application rate of nitrogen fertilizers to maintain higher photosynthetic rates and improve nitrogen uptake efficiency. Since many studies initially supported increasing yields by applying synthetic fertilizers and pesticides and irrigating arable lands [ 14 ], these were reasonable management approaches to help increase yields. However, these practices were not holistic and eventually became counterproductive as they degraded soil quality over time [ 14 ]. They did not factor in the importance of soil types and health on a plant’s productive capacity and ability to adapt to regional soils and changing climates.

Soil quality is an integral factor that should be incorporated into helping increase crop yields [e.g., 7 , 14 , 16 ]. For example, Fan et al. [ 14 , 16 ] reported that low-productivity soils reached yields of <1,500 kg ha -1 while high-productivity soils had five times higher yields (>7,595 kg ha -1 ). They attributed these greater yields in the high-productivity soils to the quality and health of the soil. Also, Fan et al. [ 14 ] suggested that a lack of organic matter content of the low- and high-productivity soils would have to be alleviated to increase crop adaptation to their site; both soil types had 25 to 50% less soil organic matter than arable soils in European countries and the U.S. [ 14 ]. Further, Jiao et al. [ 16 ] wrote how high-quality soil increased the resilience of cereal crops to climate change variability and improved yields by 0.5 to 4.0% compared to low-quality soils. The Fan et al. [ 14 ] study highlighted the importance of selecting crops that can phenotypically adapt to the soil and micro-climate while overcoming any growth-limiting conditions unique to the site, especially under a changing climate scenario. It also supported the need to manage soil quality and recognize each location’s inherent constraints beyond the resource delivery capacity of the soils.

Gaps in assessing crop yields by focusing on photosynthetic and nitrogen-use-efficiencies (NUE)

Crop genotypes with high photosynthetic and nitrogen-use-efficiencies (defined as the ratio of the nitrogen taken up by a crop to the total input of fertilizer nitrogen) are the preferred plants to grow since the assumption was that the total amount of carbon fixed by a plant will determine its potential yields, especially when nitrogen fertilizers are applied in sufficient quantities to maintain high photosynthetic rates [ 17 ]. Research has supported these assumptions. For example, Li et al. [ 18 ] reviewed 130 publications that assessed data on yield, shoot biomass, and nitrogen concentration that suggested the genetic transformation of crops (rice, maize, wheat) impact their nitrogen-use-efficiency (NUE) in different soil fertilization conditions. The latter study showed that genetic improvement in NUE significantly increased the grain yield of crops. However, this study also reported that potted experiments have a higher yield variance than field-grown crops [ 18 ], which suggests that other factors in field experiments limit plant growth and carbon and nitrogen assimilation efficiencies. Thus, the standard metrics to estimate potential yields are not sensitive predictors of growth rate changes under variable field conditions and climates.

Today, focusing on applying nitrogen fertilizers to increase photosynthetic efficiencies is seldom attainable by itself since ecophysiological and site factors limit a crop’s NUE. Simkin et al. [ 12 ] described the challenge of feeding the world by needing to increase yields by 40% through improvements in photosynthetic efficiency since “… as much as 50% of fixed carbon is lost to photorespiration…” Gutschick [ 19 ] suggested that respiration should become a focus of increasing yields since two-thirds of the original photosynthate goes into maintenance and operational costs during a grain crop’s entire growing season. Managing respiration to increase growth rates by reducing photorespiration; however, ignores its other essential role in plant physiological functions. For example, plants must continue to produce antioxidants to protect against reactive oxygen species when excitation energy cannot dissipate during drought as stomata are closed to conserve water [ 20 ]. This supports that there are limits to how much science can manipulate a crop’s genotypic potential without resulting in unintended consequences on yields. Further, these studies support what Boyer [ 21 ] wrote in 1982 that a crop only reaches 30% of its genetic potential.

Since the genetic potential of a crop results in a plant specialized to grow under specific site growth conditions, plants are less able to adapt to growth conditions that change due to unpredictable temperature and precipitation regimes. Farmers need to select crop plants capable of growing in specific soil and micro-climate niches but also to possess the traits to adapt to short-term changes in environmental conditions, e.g., phenotypically adapting by allocating energy to plant parts such as roots to acquire a growth-limiting resource such as water during short-term droughts. Plants that are specialists in adapting to site-level conditions must also be generalists adapting to unpredictable temperature and precipitation regimes.

Rizzo et al. [ 22 ] used an extensive database from 2005 and 2018 to show a plant’s genetic potential explained the smallest fraction (13%) of crop yields and management practices explained 29% of the yields in Nebraska, United States (13% of the increased yield was associated with addressing a crop’s genetic potential, 48% were correlated to decadal-level climate change, while agronomic improvements in management explained 29% of the yield increases). In industrial farming, a crop’s genetic potential allows it to efficiently grow in a specific site-level condition, while a crop’s phenotypic potential is met by farm managers, not the plant, who become the adaptive agents mitigating a plant’s inability to adapt to a changing growth environment by fertilizing, irrigating or applying pesticides [ 22 – 28 ]. The Rizzo et al. [ 22 ] study suggested that farmers can manage about 42% of the factors explaining yield levels, while climate change accounted for almost half of the yields reached by crops and has been the most problematic to manage.

Since multiple variables may explain significant changes in yields, it is challenging to identify which variables or combinations of variables describe changes in crop yields without introducing other unintended limitations to growth or environmental pollution [ 29 – 30 ]. Others recommended studying NUE more holistically, including the process of the crop acquisition of soil nitrogen. For example, Govindasamy et al. [ 26 ] suggested the initial increases in NUE were due to indigenous soil fertility levels increasing N uptake by crops. Improving NUE continues to be a focus to increase crop yields due to high N fertilization applications, which frequently results in water eutrophication and soil pollution [ 26 , 28 , 31 – 32 ]. Congreves et al. [ 33 ] reviewed NUE definitions and indices and what is currently ignored in the traditional index, such as “accounting for a wider range of soil N forms, considering how plants mediate their response to the soil N status, including the below-ground/root N pools, capturing the synchrony between available N and plant N demand, blending agronomic performance with ecosystem functioning, and affirming the biological meaning of NUE.” Therefore, focusing on only one or two variables to help increase yields across large heterogenous landscapes will probably not be efficient since other edaphic and environmental factors will become important controlling factors of crop growth within a large landscape, affecting the yields disproportionately.

It would be essential to understand and include a plant’s C allocation to growth and maintenance functions to assess the efficacy of an assessment protocol to measure changes in a plant’s NUE. For example, Asibi et al. [ 32 ] wrote how the overuse of N fertilizers resulted in low NUE when there was no simultaneous accounting for increased water-use efficiency. Cassman et al. [ 24 ] wrote, “Trends in NUE and the cultivated area will ultimately determine global N fertilizer requirements and the risk of N losses to the environment.” Currently, the dominant factors that increase potential crop yields are selecting cultivars through genetic improvements and improving NUE and water-use efficiencies while decreasing the negative impacts of high fertilizer application rates [ 24 , 34 ]. And importantly, over the last five decades, forest research has provided insights into how plants phenotypically adapt to N fertilizer additions in natural environments [ 35 ], which could help frame crop research.

It follows then that modifying NUE by adding N fertilizer to the soil to increase crop yields should involve holistically monitoring soil health for any degradation, subsequently leading to decreases in crop yields. For example, Fan et al. [ 14 ] and Jiao et al. [ 16 ] wrote how fertilizer applications in China increased from 1 t ha -1 in 1961 to 6 t ha -1 in 2015, resulting in increased grain productivity and grain yields in about half of the countries’ arable land area. But, to reach these yield increases, China consumed 35% of the global fertilizer and increased arable land irrigation by 32% while becoming the second-largest producer and consumer of pesticides, accounting for 14% of global use [ 14 ]. Despite fertilizer applications, irrigating crops, and controlling pests and pathogens, the yields of cereal crops decreased in China from 4% in the 1970s to 1.9% in the 1990s [ 14 ]. They wrote that the amount of nitrogen recovered in the aboveground crop biomass was 35% in the 1990s but declined to 28.3% for rice, 28.2% for wheat, and 26.1% for maize, all these values are lower than world averages of 40–60%. China represents an example of the inevitable holistic trade-offs that need to be anticipated. That is, yield optimization means that there will probably be interactive effects on other, potentially unknown, agricultural economics and ecological processes that would affect future yields.

The introduction of synthetic N fertilizer applications by the Green Revolution were essential to achieve high crop yields; however, understanding how N fertilizer applications impact crop growth holistically is incomplete because crop management and efficiency are based mostly on monitoring harvested product yields and not total plant productivity or edaphic conditions. For example, plant growth rates can significantly increase when soil N levels are low [ 36 ] because plants phenotypically adapt by growing more roots.

Further, N-fertilized crops need a higher application rate of pesticides to protect the yield [ 37 ] since C allocation to defensive plant chemicals decreases. Fürstenberg-Hägg et al. [ 38 ] reported that plant defenses and insect herbivory pressure have metabolic costs. The plant must produce physiologically expensive defensive chemicals using photosynthetically fixed C, reducing its growth and development. (see Martinez et al. [ 37 ] for a review of all the links between N fertilizer and pesticide applications and their unintended impacts on ecosystems, wildlife, and people). The C allocation trade-offs that a plant experiences are highlighted by addressing a plant’s response to herbivory. These are not reflected in the total photosynthate produced but are part of the within-plant C allocation patterns directly impacted by N fertilizer applications. When a plant needs to defend itself, it does not grow more roots to adapt to a drought or acquire more nutrients. These trade-offs, therefore, support understanding and managing the source-sink relationships of a total crop and not just the harvested product, which better reflects the relationship of a plant’s phenotypic plasticity to its soil and environmental conditions.

Science-practice gaps in assessing crop yields: Source-sink relationships

The harvest index in seed-producing crops is a C-centric approach that dictates that total shoot dry matter determines aboveground “sources” of photoassimilate, and harvested grain represents the “sinks” [ 27 ]. The harvest index also has a C-centric view of yield despite the variation in yields arising from the diversity of soil and climatic environments in which the crop grows. As the harvest index varies with differences in crop management [ 39 ], selecting a harvest index likely guarantees a high yield potential only under the environment for which it is selected to plant. The success of this approach at the local level will require managerial diligence and high effort while also being capable of flexibility in the face of climate change. Ultimately, crops are planted in diverse soil and environmental conditions, suggesting a crop cultivar may not perform well in many areas where it is grown. The interaction between harvest index and ecological variation in the growth environment is complex and may not scale according to total yield. However, since harvest-index increases are limited by source and sink strengths, these relationships may provide a valuable tool to identify which cultivars would grow best under different soil and climatic conditions.

The harvest index has a theoretical maximum, and there is a level at which a plant needs to grow more shoot biomass to achieve higher yields [ 40 ]. To optimize crop yields, each plant must produce leaves and roots to capture light and assimilate water and nutrients to form the stem to support the leaf canopy, especially flowers and grain. For example, leaf photosynthesis strongly correlates with increased foliar and total plant biomass [ 41 – 42 ] but less with how a plant allocates carbon since crops are generally selected for higher yields and lower root biomass [ 43 ]. A study conducted in the Midwest United States reported that maize had 8.3% of its total biomass in roots and 52.1% in grain yield at maturity, while soybeans had 16.6% in roots and 41.0% in grain yield [ 43 ]. The high percentage of grain yields does not represent a crop with an evolutionary balanced source-sink relationship where crops adapt to their growth environment. It also demonstrates how selecting traits to maximize crop yields reduces the crop’s ability to adapt to its changing growth environment phenotypically. Climate change may disrupt other normal growth conditions besides temperature and precipitation. It includes novel stressors such as high winds, which would drive carbon allocation to stems or root biomass or increase secondary metabolites in response to pests or other complex combinations of unique factors [ 44 ].

A greater understanding of the source-and-sink relationships of the plant facilitates understanding how a plant shifts its allocation to source and sink functions following fertilizer application and whether a plant can adapt to climate change at the site level. This is not just the total amount of C that is fixed and allocated to the harvested product but whether sufficient C is allocated to nutrient and water uptake or to defensive compounds if pests or pathogens attack [ 35 , 45 – 46 ]. Today, meeting the challenge to improve crop productivity requires increasing our understanding of total crop growth (above- and belowground) on different soil types and the impact of environmental stressors such as climate change on crop yields. There will be unique combinations of conditions that exhibit dynamic interactions at the site level that will drive the ecological response ( Fig 1 ). This is especially important to include in a framework assessing how to improve crop yields where, for example, irrigation may increase yield, but N fertilization may decrease yield. Decreased yields resulting from N fertilization may represent changes of the within C allocation fluxes in plants, e.g., between growth and maintenance to above- and below-ground plant parts (e.g., fruit, seeds, leaves, roots, and defensive chemicals). For example, Baslam et al. [ 29 ] explained how increasing N levels might reduce crop yields as plant shoot growth increases with less C allocated to roots.

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[ 1 ] Sources; the dominant factors that influence nutrient availability, [ 2 ] Controls; genomic mechanisms of plant adaptation to disturbance in its environment, and [ 3 ] Sinks; plant carbon sink strength as a function of metabolism. The major functional processes associated with each listed at bottom. The plant images are from unknown artists; (left) maize plant (Gong F, Wu X, Zhang H, Chen Y and Wang W (2015) Frontiers | Making better maize plants for sustainable grain production in a changing climate (frontiersin.org ) and (center) phenotypic plasticity of roots (Calleja-Cabrera J, Boter M, Oñate-Sánchez L and Pernas M (2020) Frontiers | Root Growth Adaptation to Climate Change in Crops (frontiersin.org ) are both licensed under CC BY . The chemical structure of the secondary metabolite was recreated by the author.

https://doi.org/10.1371/journal.pstr.0000122.g001

Measuring changes in photosynthesis may not detect changes in plant growth due to drought, low temperature, and nutrient limitations [ 47 ]. For example, a drought will inhibit cell growth and tissue formation before C uptake is inhibited during photosynthesis. Also, meristem cell production will stop at temperatures ≤ 5°C, while net photosynthesis continues at 50–70% of the rate when plant growth occurs above 5°C [ 47 ]. Körner [ 47 ] supports understanding how above- and belowground growth changes in relationship to drought, low temperature, and nutrient limitations since these variables impact plant growth much earlier than photosynthetic efficiencies. Holland et al. [ 48 ] further suggested using a theoretical analysis approach to explore how root and leaf respiration can explain C allocation strategies by increasing the timing of C assimilation to leaves and roots. Their study supports using models to understand growth and recognize that maintenance costs can also enhance yields.

These studies support the need for more research on the interplay of physiological processes, such as how allocation shifts between the shoots and roots are involved in increased water and NUE, while also decreasing pollution from high fertilizer applications. Most experimental field studies emphasize taking measures of seedlings under controlled conditions and modeling crop yields that do not include realistic estimates of how crops allocate C to all the C pools and fluxes [ 49 ]. For example, selecting a crop to become more drought tolerant may reduce its yields as there is insufficient C fixed during photosynthesis to balance all the source-sink relationships that are the adaptive mechanisms in response to climate change.

Ecosystem translation of the belowground world to illuminate potential crop yields

If improving photosynthetic efficiency and NUE successfully address all the growth-limiting factors in agriculture, crop yields will probably not plateau or decrease in many parts of the world [ 6 ]. Photosynthetic efficiency is crucial since it determines the size of C pools available for improving crop yields. However, within plant source-sink relationships will determine whether a crop can grow and maintain its tissues and adapt to increase its assimilation of resources that limit its growth. Thus, it will decide if a crop will reach its growth and yield potential [ 27 ]. Roots are essential in determining how much water and indigenous soil nutrients and N applied in fertilizers are assimilated by a crop plant. Also, roots are the interface exposed to moderately-to-severely degraded land and determine whether crops can grow on a site; Iseman and Miralles-Wilhelm [ 50 ] reported that 52% of the global agricultural soils are moderately to severely damaged.

There is increased recognition of the importance of roots in crop adaptation to their changing environment and that N fertilizer decreases root growth [ 34 , 45 , 51 – 54 ]. Previously, the assumption was that knowledge of aboveground growth is a surrogate for the total plant response to its dynamic environments. However, Liu et al. [ 54 ] synthesized 88 published studies to show the existence of a phenological mismatch in the timing of above- and below-ground growth in response to climate variability. Therefore, we must implement a holistic view of the plant adaptive response at multiple levels of biological organization, from resource allocation to phenotypic growth patterns, and more fully relate the emergent properties that arise under different environmental and ecological conditions to the individual agricultural system components.

Further, the increased frequency of droughts is impacting yields. It highlights the role of roots in a crop adapting to short-term changes in available water supplies and nutrient acquisition. When less photosynthate allocation to roots occurs with higher N applications [ 35 , 55 – 56 ], a plant may produce less biomass, i.e., lower productivity levels, since plants with smaller root systems have less capacity to acquire soil nutrients and water during a drought. Maslard et al. [ 53 ] described how selecting for a diversity of optimal root systems in new soybean varieties is important to address edaphic and climatic limits to crop growth, e.g., cultivars with deep root systems to take up N and water from lower soil layers and cultivars with shallow root systems that can readily acquire nutrients from surface soils such as when phosphorus availability is limited. The total biomass of nine different soybean genotypes by Maslard et al. [ 53 ] had statistically significant differences in total plant, shoot, and root biomass, showing how selecting a crop by its root biomass is helpful information for managers.

Olagunju et al. [ 52 ] reported how tropical upland rice adapts to dynamic climatic conditions such as unpredictable rainfall and the resulting droughts by reducing biomass allocation to shoots but not to roots. They further showed that upland rice adapted to these periodic drought events depending upon how soil texture impacted root and shoot growth, i.e., less root biomass is produced as the amount of clay increases in the soil. Olagunju et al. [ 52 ] wrote that focusing on the plant’s reproductive parts would result in “more reliable estimates for identifying rice cultivars with higher yield potential at harvest.” They also wrote, “Soil environment that promotes greater allocation of biomass to reproductive structure through a restriction in the expansion of vegetative organs is well suited for upland rice cultivation.” A focus on selecting cultivars for their reproductive yields makes the crop susceptible to the unpredictable rainfall periods that a balance of root biomass and crop yields would not produce.

In addition to observed responses, climate variability may also induce unpredictable responses that will vary by the local milieu of growth conditions. For example, higher soil temperatures are associated with less allocation to root biomass as feedback to changes in soil condition, including loss of moisture, aeration, and nutrients [ 56 ]. Calleja-Cabrera et al. [ 45 ] wrote about the need to develop an efficient root system that can make a plant better adapted to its site to increase crop productivity. They wrote how increasing temperatures with climate change increases the stresses that a crop will experience and will have to adapt to, e.g., “drought, salinity, nutrient deficiencies, and pathogen infections” [ 45 ]. In that case, a crop plant that can adapt to its growth environment should be selected, which means data needs to be collected on total plant biomass and carbon allocation to roots and defensive compounds [ 35 , 46 ]. This selection process must account for different types of variability, the magnitude of change among the most important growth parameters, and how well the cultivar is expected to respond across a diversity of conditions. These carbon allocation shifts in response to site edaphic and micro-climatic factors ultimately determine how well a plant grows at a local site and how much crop yields can increase when growing under dynamic environmental conditions [ 35 , 57 ].

Cakmak et al. [ 58 ] wrote how a balanced fertilizer application is needed to maintain growth since mineral deficiencies of phosphorus, potassium, and magnesium impact C partitioning differently between roots and shoots in bean plants. Their study showed the roles of magnesium and potassium in allocating C from shoots to the roots. Woo et al. [ 34 ] showed how managing wheat yields to achieve a root radius of 0.1 and 0.3 mm resulted in optimal wheat yields. Since many other factors impact roots, managing fertilizer application rates may not support root growth architecture in the field [ 34 ].

Instead of focusing on the product’s increased yield, a farmer can select a crop based on variable rooting depths to address site scale limitations to growth during dynamic climatic conditions. The importance of selecting genotypes based on their root architecture and drought tolerance, as well as the role of roots in increasing soil organic matter levels, is stimulated by the recognition that “allocation pattern indicates environmental plasticity to soil properties, temperature and soil water availability” [ 51 ]. Mathew et al. [ 51 ] reported how drought stresses reduced total biomass production by 35% and root-to-shoot ratios by 14% and how soil C is mainly derived from root activity and decomposition of root tissues. These are subtle and specific observations of the plant response, and as we learn which responses to measure, we will fine-tune the site-level accommodations required and the selection of varieties that can meet the specific demands of the site.

Selection of plants based on their root architecture still needs to factor in the continued use of fertilizers in farming. This means that when a plant is bred for its increased allocation of photosynthate to the harvested product, it may be less able to maintain and protect plant tissues or acquire other limiting resources needed to grow [ 59 ]. This occurs when a crop is selected to optimize one part of the plant, e.g., genotypes high in seed oil and protein content for human consumption, animal feed, transport fuels, and many other products. Under these circumstances, the plant allocates less to defensive chemicals. The farmer must spray herbicides and pesticides to reduce the growth of other competitive plants and to protect the plant due to the tradeoffs that were considered acceptable to management when those protective traits were discounted for the artificially selected desired traits.

A holistic framework: Thresholds of total productivity and yields due to growth-limiting factors

However, one still needs a framework to determine how much site growth limiting factors reduce a crop’s total productivity to determine the potential growth and yields possible per site. This concept supports using a metric—e.g., a plant’s total productive capacity—to estimate whether a crop is close to a threshold of decreasing productivity with additional growth-limiting factors. This index assesses a plant’s growth in its edaphic-climatic environment and allows cross comparisons of different sites [ 55 ]. A module, like Ecosystem Fit (eFit), must be created to assess whether a plant can continue adapting its potential productivity in response to site growth-limiting conditions. Since eFit uses parameters of solar radiation, temperature changes, edaphic and climatic factors, it may be useful to reflect or index growth rates or site productivity and compare it to other sites with different conditions. In fact, indices like this could be used to tease apart specific site factors that affect productivity.

A farm needs to be viewed at an ecosystem-level and based on total productivity measures, especially since crop yields may be a third of the total C fixed during photosynthesis [ 22 ]. A plant’s total net primary productivity (tNpp) represents how much C can be allocated to acquire growth-limiting resources, such as nutrients and water, in dynamic soil and climatic conditions. In addition, the crop adapts to grow and store carbohydrates to protect itself during disturbances. A crop plant should not be decoupled from its growth environment since management will not be able to address all the limitations to growth that a crop plant will experience. This suggests monitoring photosynthetic efficiencies, and NUE needs to be replaced with a holistic approach that assesses the entire ecology of the crop. We currently don’t have the frameworks to assess how management can counter yield decreases locally, especially since crop growth is generally viewed through a narrow lens that looks at only part of the aboveground portion of the plant. This narrow focus does not include site-level soil and climatic factors which are crucial for understanding the totality of factors that explain decreases holistically at the local-level. For example, it does not factor in the potential negative effect of high levels of N fertilizer which can decrease the plant’s ability to acquire nutrients from unhealthy soil.

The new framework to measure the phenotypic plasticity of a plant first emerged from Gordon et al. [ 60 ], who used the estimated Theoretical Maximum Productive Capacity (TNpp max ) using the Loomis and Williams [ 61 ] method. Gordon et al. [ 60 ] developed a conceptual approach to calculate the eFit or the potential total productivity for a site based on the ratio of field-collected data of tNpp to the TNpp max calculated from external factors. Gordon et al. [ 60 ] described eFit, and Klock et al. [ 35 , 62 ] provided the methodology to calculate theoretical maximum productivity and eFit. In global forests, eFit and total productivity were invariant to leaf behavior traits but were “strongly dependent on temperature, precipitation, elevation, Oxisol, Entisol, and Ultisol soils, and silty loam soil texture” [ 35 ].

Gordon et al. [ 63 ] demonstrated the utility of using tNpp to calculate eFit on two crop plants grown in Japan. They calculated an eFit of 19% for maize in Iwate, Japan (actual tNpp of 18 Mg ha -1 yr -1 ) and 14% for rice (not displayed) in Akita, Japan (actual tNpp of 15 Mg ha -1 yr -1 )( Fig 2 ). In the eFit calculations made by Gordon et al. [ 63 ] and the yield data of ~5 Mg ha -1 yr -1 dry weight provided in Ritchie et al. [ 6 ], about 25% of the tNpp is allocated to the harvested product, and the remaining to plant maintenance and growth. Ecosystem fit showed that both maize and rice reached less than 19% of their theoretical maximum productivity threshold (TNpp max ). Gordon et al. [ 60 ] also showed how eFit is sensitive in describing how edaphic and climatic site conditions limit crop growth and yields, which are factors that can be managed or mitigated.

Maize in Japan using data published in Gordon et al. [ 60 ]. The potential productivity shows how much tNpp needs to increase to achieve a higher fit when ecological management alleviates the growth-limiting factors constrained by soil health and climatic factors. [Calculate eFit = tNpp observed/TNpp max * 100] [Globally, dry weight yields of maize are about 5 Mg ha -1 yr -1 [ 6 ] which is ~ 25% of total actual net primary productivity (tNpp).]

https://doi.org/10.1371/journal.pstr.0000122.g002

Definitions of yield and productivity

There is confusing terminology in the literature on what is included in the terms “yield” and “productivity” of a crop. Yields are generally related to agricultural production but are not synonymous with productivity that ecologists would use. For example, yields are the usable measured biomass or weight or also volume of the crop harvested, such as the grain, cereal, and fruit produced per unit of land [ 64 ]. Further, if yields are given in weight terms, the weights are moist weight values and are reported as a standardized moisture content, which is important for proper grain storage and comparison across other research trials [ 65 ]. It does not include the weight of any other part of the plant. The actual yield on a farm varies depending on the amount of sunlight or radiation reaching the plant, the plant’s water and nutrient uptake efficiencies, the crop’s genetic potential, and how much pests and pathogens decrease the crop product harvested.

Yield data does not provide as much information as the tNpp of the whole plant based on dry weight measures of biomass. For example, the productive capacity of a plant can indicate whether it has the potential to adapt to its dynamic environment and grow biomass at the site level. Additionally, since fertilizer applications generally decrease [ 55 ] the C allocated to the root system, it would follow that just being attentive to increasing yields would not allow managers to recognize that potential decreases in root biomass could significantly increase the risk of the crop’s ability to respond to lower soil moisture during droughty conditions and decreasing its yields. This would be even more important to consider for perennial crops. The question is whether long-term yields can be improved by selecting crop varieties based on their tNpp while balancing its allocation of C to the harvested product and allowing a plant to adapt to site-level edaphic and micro-climatic constraints and continue to grow.

In this paper, we will consider agricultural yields in terms of product biomass, but they are also known in terms of economic returns and produced on a per-unit-of-land basis [ 64 ]. It differs from total crop productivity (e.g., tNpp) measured by ecosystem ecologists as Mg ha -1 yr -1 , which represents a plant’s annual total biomass productivity. The ecological definition includes the plant’s above- and belowground parts (e.g., leaves, branches, stems, bole, coarse roots, fine roots, mycorrhizas), and biomass loss due to herbivory, autotrophic respiration, and root turnover and litterfall (e.g., carbon fluxes) added back to biomass carbon pools (e.g., [ 66 – 67 ]).

Knowledge of a crop’s actual tNpp and TNpp max will provide a framework to correlate yields to changing environmental and edaphic conditions represented across a heterogeneous landscape. Actual tNpp, in dry-weight biomass produced during a year, provides an index of growth that can be compared across a diversity of sites. In this paper, we present different productivity terms that are used in our framework. Actual Productivity of a crop is the total Net Primary Productivity (tNpp observed or measured) reached at a site as constrained by soil health, pests and pathogens, drought, temperature, and salinity, to name a few variables. The Potential Productivity (tNpp potential) is the amount that tNpp can be increased using management, i.e., environmental and/or ecological tools, such as those that can enhance or create healthier soils or use more adaptable plants for site-specific conditions. This is determined from field-based research that monitors tNpp changes under different management conditions. Calculating the theoretical maximum productivity (TNpp max ) provides the maximum productivity attainable at a given site, but it is not environmentally or ecologically possible to manage since it would involve managing limiting-growth variables that cannot be economically manipulated. Examples of the variables used to calculate TNpp max for a given site include solar radiation, temperature, and the growing season length, which are factors we cannot manage.

Characterization and definitions of the dominant soils in India (UN FAO)

• Cambisols—are young soils, medium and fine-textured, shallow topsoil depth; moderate fertility but commonly deficient in phosphorous (P) and calcium (Ca); high erosion rates, generally good water-holding capacity, good internal drainage, but the dried soil surface becomes extremely hard when dry, hindering root growth and favoring erosion.
• Fluvisols—are young soils forming in alluvial deposits with little or no profile development, mineral soils conditioned by topography; in coastal areas, they have high levels of salts and aluminum (Al) ions—therefore, low soil pH (i.e., high soil acidity) and high Al toxicity both help create phosphorus deficiency and also N deficiency is common; found in floodplains so they periodically flood and therefore need flood control, drainage or at times even irrigation.
• Luvisols—are moderately weathered soils, and if they have clay-enriched subsoils, they have high cation exchange capacities and high base saturation; steep slopes are not uncommon and need erosion control; high nutrient content, therefore are fertile and widely used for agriculture; well-drained but soil may become saturated with water for extended periods potentially needing drainage.
• Nitosols—are strongly weathered soils but are more productive than most red tropical soils and are deep soils with favorable physical properties with deep rooting so they are resistant to erosion; contain low-activity clays (i.e., have a lower capacity to retain and supply nutrients), high P fixation enhanced by iron/aluminum (Fe/Al) chemistry; generally fertile soils despite low available P and low base status but need to add P fertilizer; plant available nutrients are fairly deep (~150 cm), they are well-drained but total moisture storage is good because they are deep; however they are hard when the soils are dry. They are exploited widely for plantation agriculture.
• Vertisols—have high content of shrink and swell clays that are strongly impacted by wet and dry conditions (i.e., harden when dry and become sticky when wet), little textural differences by depth; good for mechanized farming if the rainfall is high or they are irrigated; many areas are not farmed because they would need to be irrigated; low soil permeability, so irrigation may cause waterlogging. They are best suited for pastureland use and cultivating plants, such as rice, that thrive in standing surface water.
• Xerosols—are desert soils that have mostly sandy soil and are in low rainfall areas; so, they have low N and organic matter (OM) but high concentrations of calcium carbonate and soluble salts and phosphate, therefore they are frequently infertile requiring substantial management. Generally, they have soil moisture deficits and are susceptible to wind erosion, so they are unsuitable for rain-fed agriculture. But if irrigated, these soils may be among the best soils for farming. Generally, these soils are of little or no value for agriculture due to the lack of rainfall.

Source: https://www.britannica.com/science/soil/FAO-soil-groups , https://www.isric.org .

Comparison of the dominant soil groups, mean annual precipitation, mean grow temperature, and Theoretical Maximum Productive Capacity (TNpp max )

The seasonal grow temperature was strongly associated with latitude ( r = –0.67, t = –16.874, df = 354, p < 0.001) whereas TNpp max was independent of latitude ( r < 0.001, t = 0.002, df = 354, p = 0.99), even though the latitudinal range of this study was ~25° (8.31° N– 32.80° N), nor was TNpp max associated with longitude ( r < -0.075, t = -1.777, df = 354, p = 0.16) where the range was ~19° (69.47° E– 88.59° E).

Unsupervised cluster analyses identified a single threshold for grow temperature of 26° C and two relatively homogenous clusters of TNpp max , one on either side of 289.1 Mg ha -1 yr -1 , thus data summaries were made for two TNpp max groups, classified as low (≤ 289.1 Mg ha -1 yr -1 ) and high (> 289.1 Mg ha -1 yr -1 ). We then utilized a series of parametric and non-parametric approaches to understand the association between TNpp max , tNpp, and eFit and dominant soil groups, and climatic variables.

The statistical power of the low and high TNpp max groups to soil type was low for a meaningful comparison ( power < 0.24), therefore we aggregated TNpp max . An omnibus Welch’s heteroscedastic F Test for the data ( n = 356) with post hoc Bonferroni correction for multiple pairwise comparisons found differences of at least one soil group with an effect size (ω 2 = 0.35 CI 95% [0.18, 1.00]) considered moderate per Cohen’s 1988 convention [ 68 ] ( Fig 3 ). As variability of TNpp max among the dominant UN FAO soil types was not correlated with geography, we fitted a generalized linear model (estimated using ML) to predict TNpp max with dominant soil type to identify which soil types were the drivers of the association ( Table 1 ). The model’s total explanatory power was low ( R 2 = 0.14) with an intercept corresponding to Cambisols of 291.91 (95% CI [290.2, 293.7], t (350) = 324.09, p < .001). Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p -values were computed using a Wald t-distribution approximation.

The large dot is the mean and reported adjacent to the plot. The thick horizontal line is the median. The box in the center represents the interquartile range, and the vertical thin gray lines above and below the box represent the rest of the distribution. The grey dashed line represents the grand mean. The totality of the curved shape represents a kernel density estimation where wider sections represent a higher probability of a given value and thinner sections represent a lower probability. Results of an omnibus one-way ANOVA, partial effect size, and number of observations reported at the top.

https://doi.org/10.1371/journal.pstr.0000122.g003

https://doi.org/10.1371/journal.pstr.0000122.t001

Another approach was to determine whether TNpp max varies among the dominant soil types by grow temperature. The estimation of TNpp max is based on incoming solar radiation and the mean growing temperatures for the total month-days with temperatures above 0°C. Therefore, the maximum theoretical potential for growth, i.e., TNpp max may be affected by environmental factors such as elevation or azimuth of a given site, indicating some amount of top-down control by topography.

There was a significant negative relationship between TNpp max and the grow temperature suggesting reduced crop productivity as temperatures increase among Luvisols, Nitosols, and Vertisols ( Fig 4 ). The soils that showed no relationship with temperature are also less suitable for agriculture due to nutrient deficiencies (Cambisols, Fluvisols) and low precipitation (Xerosols). The variability of TNpp max was highest among Luvisols ( var = 93.9), Fluvisols ( var = 121.4) and Nitosols ( var = 132.1), while it was lowest for Cambisols ( var = 29.4), Xerosols ( var = 35.7) and Vertisols ( var = 51.1) ( Fig 4 ).

Association between TNpp max to the average grow season temperature among each dominant soil type. A temperature threshold of 26° C is indicated by the dashed red line, and the Pearson’s Product Moment correlation ( r ) with p -value is reported at the top for each soil type.

https://doi.org/10.1371/journal.pstr.0000122.g004

Comparison of TNpp max to mean annual precipitation indicated how the dominant crop growing areas varied between precipitation levels (i.e., dry to wet precipitation groups). Half of the dominant soil types indicate non-significant relationships, therefore maintaining a focus on selection for crops with higher photosynthetic efficiency is not going to provide additional productivity benefits ( Fig 5 ). Most notably, TNpp max was primarily concentrated in a range of ~280 to 300 Mg ha -1 yr -1 , but above a threshold of ~1,200 mm of annual precipitation, there was increased variability of TNpp max among Fluvisols and Nitosol dominated sites probably due to specific climatic niches. In contrast, crops growing in Cambisols, Luvisols, Vertisols, and Xerosols are located in areas that have high TNpp max , >300 Mg ha -1 yr -1 . Xerosols and Cambisols were the only soil groups identified in the dry precipitation group ( Fig 5 ).

Four precipitation classes; dry, moist, very moist, and wet mean annual precipitation are from Vogt et al. [ 69 ]. The optimal range of precipitation for agricultural productivity is indicated by the grey panel (500–1200 mm -1 yr -1 ). Pearson’s Product Moment correlation ( r ) with p -value is reported at the top for each soil type.

https://doi.org/10.1371/journal.pstr.0000122.g005

This comparison between TNpp max and annual precipitation further supports the need to characterize the site-level edaphic and micro-climatic conditions since total productivity is related to the available water supplies and the nutrient delivery status of the soil. The highest variability in TNpp max was found in the very moist group, ~1,200 mm yr -1 precipitation, where TNpp max showed a bifurcated response. For example, Ferric Luvisols had a linear decline and linear increase, whereas crops growing on Chromic Luvisols would be limited in available energy, limiting their adaptive capacity. In contrast, areas of agriculture in the moist and lower threshold of the very moist precipitation groups show more consistent TNpp max values [Note: Comparisons between Ferric Luvisols and Chromic Luvisols are not included in this paper]. This suggests that when agricultural areas receive greater than ~1,200 mm of rainfall annually, these represent extreme sites for agriculture with a higher likelihood of uncertainty in yields.

Comparison of the dominant soil groups, mean annual precipitation, mean grow temperature, and Total Net Primary Productivity (tNpp)

An unsupervised cluster analysis identified three natural clusters of tNpp classified as (low < 5.12, medium > 5.12 & < 9.51, and high > 9.51 Mg ha -1 yr -1 ). An omnibus Welch’s heteroscedastic F Test for the tNpp clusters found significant differences among the six dominant UN FAO soil types in the low and medium tNpp groups, whereas post hoc Bonferroni correction for multiple pairwise comparisons resulted in no evidence of significant differences in the high tNpp group ( Table 2 ).

Soil groups with ( n ≥ 8). Letters indicate column-wise comparisons among the soil groups (where the mean of tNpp does not differ among soil groups displaying the same letter). Aggregated crop lands (bottom row).

https://doi.org/10.1371/journal.pstr.0000122.t002

A summary comparison between tNpp and the soil types identified Luvisols and Nitosol soil types as having the highest range of tNpp values (there was no low range of tNpp observations among Nitosol soil types) ( Table 2 ).

The lowest tNpp were recorded in Xerosols (tNpp range 0.0–6.4 Mg ha -1 yr -1 ), Vertisols (1.0–13.0 Mg ha -1 yr -1 ) and Cambisols (0.2–9.8 Mg ha -1 yr -1 ). The highest tNpp ranges also had higher eFit values and ranges: Fluvisols (0.9–4.7%), Luvisols (0.5–6.6%), and Nitosols (2.1–8.9%). The lowest eFit means were recorded in Xerosols, Vertisols, and Cambisols soil types (UN FAO classification). The variability of eFit was highest with Luvisols and Nitosols, indicating a wide range of productive capacity where there may be opportunities for increasing yields ( Table 2 ).

We also explored whether productivity varied significantly among the dominant soil types by fitting a linear model (estimated using OLS) to predict tNpp with soil group. The model explains a statistically significant and substantial portion of the variance ( R 2 = 0.38, F (5, 350) = 43.03, p < .001, adj. R 2 = 0.37). The model’s intercept, corresponding to Cambisols, is at 4.31 (95% CI [–3.64, 4.98], t (350) = 12.68, p < 0.001, AIC = 1844). To identify the influence of climatic variables to the response we then fitted a linear mixed model (estimated using REML and nloptwrap optimizer) with grow temperature and precipitation as fixed effects and dominant soil group as a random effect. The model’s total explanatory power was substantial R 2 = 0.47 with the fixed effects accounting for R 2 = 0.14. The model’s intercept corresponding to precipitation and grow temperature = 0 is at –12.06 (95% CI [–18.55, –5.58], t (351) = –3.66, p < .001). Within this model: the effect of temperature is statistically significant and positive (beta = 0.64, 95% CI [0.40, 0.88], t (351) = 5.21, p < .001, Std. beta = 0.21), the effect of precipitation is statistically significant and positive (beta = 2.06 −03 , 95% CI [1.38 −03 , 2.74 −03 ], t(346) = 5.97, p < .001, Std. beta = 0.28). The model was slightly improved (AIC = 1818).

A statistical analysis of the high and low TNpp max groups to the six dominant UN FAO soil types by one-way ANOVA found significant differences between at least two dominant soil groups ( Fig 6 ). The Welch’s F -test assumes that data groups are sampled from populations that follow a normal distribution but does not assume that those two populations have the same variance. Pairwise comparisons with correction for multiple comparisons indicated significant differences between varying combinations of all soil types. There were no significant differences in actual tNpp between Cambisols and Fluvisols, nor between Cambisols and Xerosols in either TNpp max group.

Comparison between (a) high TNpp max group (> 289.1 Mg ha -1 yr -1 ) and (b) low TNpp max group (≤ 289.1 Mg ha -1 yr -1 ) to the dominant UN FAO soil groups ( n > 8). Test results reported at top are for the omnibus heteroscedastic F Test with Bonferroni correction. Statistically significant pairwise comparisons are delineated by brackets with the p value reported above the line (* ≤.05, *** < .001). The large dot is the mean and is reported adjacent to the plot. The thick horizontal line is the median. The box in the center represents the interquartile range and the vertical thin gray lines above and below the box represent the rest of the distribution. The grey dashed horizontal line represents the grand mean. The totality of the curved shape represents a kernel density estimation where wider sections represent a higher probability of a given value, and thinner sections represent a lower probability.

https://doi.org/10.1371/journal.pstr.0000122.g006

The analysis identified statistical differences among varied combinations of soil types, where the effect size of the high TNpp max group was ( g = 0.82) ( Fig 6a ) and the effect size of the low TNpp max group was ( g = 0.86) ( Fig 6b ). These values are considered high per convention, indicating they are more probable under the alternative hypothesis.

We can infer that the overall relationship between soil types and tNpp are more strongly supported by the evidence than TNpp max . In this instance ( Fig 6 ), the effect size indicates that the low TNpp max group (panel b ) is more probable under the alternative hypothesis.

Similar to the TNpp max comparison to grow temperature ( Fig 4 ), a temperature threshold was found above 26°C when comparing tNpp to grow temperature ( Fig 7 ). In contrast to TNpp max , tNpp did not support a decrease in productivity as temperatures increased. In fact, tNpp was maintained and increased at temperatures higher than 27.5°C. In this comparison, crops growing on Vertisol soil type maintained a similar range of tNpp (5 to 10 Mg ha -1 yr -1 ) in the temperature range between 25.0 to 27.5°C. The highest tNpp levels were found in the Nitosol soil type, where tNpp exceeded 20 Mg ha -1 yr -1 ( Fig 7 ). Luvisols had the highest variation in tNpp compared to the other five dominant soil types (UN FAO).

A temperature threshold of 26° C is indicated by the dashed red line, and the Pearson’s Product Moment correlation ( r ) with p -value is reported at the top for each soil type.

https://doi.org/10.1371/journal.pstr.0000122.g007

In contrast to the relationship between TNpp max and mean annual precipitation ( Fig 5 ), there was no upper threshold of tNpp at 1,200 mm of mean annual precipitation. The precipitation threshold reached 1,600 mm ( Fig 8 ). Total net primary productivity varied over a range from 1 to 22 Mg ha -1 yr -1 in the Moist and Very Moist precipitation classes. This suggested that tNpp levels were not limited by precipitation levels. The highest tNpp levels were found in the Nitosols (6.0–25.4 Mg ha -1 yr -1 ) and Luvisols (1.4–21.1 Mg ha -1 yr -1 ) soil types (UN FAO), while the lowest range of tNpp values (1.0–13.0 Mg ha -1 yr -1 ) were produced in the Vertisols, Fluvisols (2.6–13.5 Mg ha -1 yr -1 ) and Cambisols (0.2–9.8 Mg ha -1 yr -1 ) soil types (UN FAO). Nitosols show optimal productivity with increasing temperature and precipitation conditions, whereas crops on Cambisols do not show higher productivity across the entire range of precipitation and temperature. Therefore, under limiting growth conditions Nitosol soils are still productive.

Four precipitation classes; dry, moist, very moist, and wet mean annual precipitation thresholds are from Vogt et al. [ 69 ]. The optimal range of precipitation for agricultural productivity is indicated by the grey panel (500–1200 mm -1 yr -1 ). Pearson’s Product Moment correlation ( r ) with p -value is reported at the top for each soil type.

https://doi.org/10.1371/journal.pstr.0000122.g008

The National Bureau of Soil Survey and the Land Use Planning under the control of the Indian Council of Agricultural Research (ICAR) have conducted extensive studies on Indian soils [ 70 ]. The ICAR classifies soils based on their characteristics as per the Soil Taxonomy of the United States Department of Agriculture (USDA). Chief characteristics are based on genesis, color, composition, and location. The three categories relevant to our reference sites were as follows:

(i) Alluvial soils, comprising ~43% of Indian soils dominate the northern plains and river valleys, and found extensively in deltas and estuaries. Alluvial soils are highly fertile, (ii) Red / Yellow soils are found largely in drier areas and are generally nutrient deficient with sandy to clayey and loamy texture, and (iii) Black soils are best for the cultivation of cotton and cover most of the Deccan plateau, which is characterized by deserts, xeric shrublands, and dry tropical forests. Although these areas receive less rainfall, Black soils have high water retaining capacity and are rich in minerals but deficient in N, P, and organic matter, with a clayey texture.

An unsupervised cluster analysis identified three natural clusters of tNpp among ICAR soil classifications as: (i) low, 0.0–5.1 Mg ha -1 yr -1 , (ii) medium, 5.2–9.5, and (iii) high, 9.7–25.4 Mg ha -1 yr -1 ( Table 3 ). The analysis found tNpp was significantly lower in the Alluvial soils (mean = 2.93, n = 50) compared to the Red / Yellow soil types (mean = 3.69, n = 26) in the low productivity group ( W Mann-Whitney = 996.00, p < 0.001). Whereas, these two groups were similar in the medium productivity group ( X 2 Kruskal-Wallis (2) = 8.88, p = 0.01), and high productivity group ( X 2 Kruskal-Wallis (2) = 8.47, p = 0.01). These comparisons did not identify the range of tNpp that resulted from using the dominant UN FAO soil types. Table 3 shows the clusters of tNpp (low, medium, high) ranked by the three major soil type groupings.

Omnibus tests by tNpp group indicated significantly higher tNpp in red/yellow soil types in the low tNpp group ( W Mann-Whitney = 996.00, p < 0.001), and differences of tNpp among the soil types of the medium tNpp group ( X 2 Kruskal-Wallis (2) = 8.88, p = 0.01), and the high tNpp group ( X 2 Kruskal-Wallis (2) = 8.47, p = 0.01).

https://doi.org/10.1371/journal.pstr.0000122.t003

These results indicate that a soil type may be more important at lower productivity levels. But, as the three India soil types include several UN FAO soil types, it was less successful in identifying low, medium, and high clusters. Each soil type was found in all clusters except for Black which is not present in the low tNpp cluster. This suggests that volcanic soils produce crops in the medium and high tNpp groupings, and these groupings were less able to identify productive agricultural fields that are using the UN FAO soil type produced ( Table 3 ). When comparing the Nitosols soil type (UN FAO) and the Black soil type, they had very similar medium and high tNpp clusters, but the other soil types did not produce similar tNpp clusters with the UN FAO soil types.

Comparison of the dominant soil groups, mean annual precipitation, mean grow temperature, and Ecosystem Fit (eFit)

All soil types had a large range of tNpp, suggesting no significant differences in eFit by dominant soil types ( Fig 3 ). Significant differences were found in eFit by the dominant soil types (UN FAO) grouped by the low and high TNpp groups ( Fig 9 ). Significantly higher eFits were found in the Nitosols soil type (UN FAO), suggesting that this soil type could potentially support higher tNpp through management. The second highest potential growth rates increases were found in the Luvisols soil type (UN FAO), also suggesting management can increase tNpp. All the dominant soil types in India had low eFits ( Fig 9 ) compared to the soils in Japan where eFit of 19% was reached, that was twice as high as eFits calculated for India (8.9%) (see Fig 2 ) [ 60 ]. The variability in eFit was the highest for Luvisols and Nitosols in India, suggesting these soil types have a greater potential for increased management to succeed in increasing growth rates ( Table 2 ).

Comparison between (a) high TNpp max group and (b) low TNpp max group to the dominant UN FAO soil groups ( n > 8). Test results reported at top for omnibus heteroscedastic F Test with Bonferroni correction (α = .05). Statistically significant pairwise comparisons are delineated by brackets with the p value reported above the line (* ≤.05, *** < .001). The large dot is the mean and is reported adjacent to the plot. The thick horizontal line is the median. The box in the center represents the interquartile range and the vertical thin gray lines above and below the box represent the rest of the distribution. The totality of the curved shape represents a kernel density estimation where wider sections represent a higher probability of a given value and thinner sections represent a lower probability. The grey dashed line represents the grand mean.

https://doi.org/10.1371/journal.pstr.0000122.g009

The TNpp max ( Fig 4 ) suggested a negative relationship between TNpp max and mean grow temperature; however, the opposite relationship was produced between Ecosystem fit and mean grow temperature ( Fig 10 ). A positive linear relationship suggests that as temperature increases, the eFit of a crop will increase due to reaching higher tNpp levels ( Fig 10 ). The significant 26°C threshold produced with TNpp max ( Fig 10 ), is not a threshold produced with eFit. The tNpp included in the eFit estimates show that the range of variance in eFit does not vary between 24°C and 29°C ( Fig 7 ). The dominant soil types support and maintain a range of eFit values ( Fig 10 ), with Luvisols and Nitosols consistently producing a higher eFit than other soil types. These results suggest that eFit is less impacted by temperature than by the dominant soil type.

An average growing season temperature threshold of 26° C is indicated by the dashed red line, and the Pearson’s Product Moment correlation ( r ) with p -value is reported at the top for each soil type.

https://doi.org/10.1371/journal.pstr.0000122.g010

In contrast to the relationship between TNpp max and mean annual precipitation ( Fig 5 ), eFit did not reach an upper threshold with mean annual precipitation at 1,200 mm ( Fig 11 ). For example, eFit varied across a precipitation range of 500 to 1,600 mm of mean annual precipitation while the thresholds of eFit were determined by the dominant soil types (UN FAO). The mean annual precipitation varied from the Moist and Very moist precipitation classes. This suggested that eFit levels were not limited by precipitation levels, and crop adaptation to its site will be less impacted by precipitation compared to other site factors. The highest eFits were found in the Nitosol and Luvisol soil types (UN FAO). The lowest eFit levels were found in the Vertisol, Fluvisols and Cambisols soil types (UN FAO).

https://doi.org/10.1371/journal.pstr.0000122.g011

Since climate change significantly impacts potential crop yields, it is important to develop a new framework to assess whether a crop plant can adapt to its site-level growth limiting factors. Today, the promise of transgenic hybrid traits and increased NUE of crop plants are not delivering the expected increases and have resulted in soil pollution and degraded soil health [ 71 ]. Since most plants do not reach their genetic productivity potential [ 58 ], it is worth exploring how well the light-use-efficiency model compares to the total net primary productivity focus to select crops to grow in diverse site conditions.

The study was designed to compare two assessment frameworks to identify which factors sensitively detect and identify the variables that determine the potential yields and productivity of a crop at the site level: (1) the light-use-efficiency model (carbon-centric model) to assessing yield potentials that included applying nitrogen fertilizers to increase a plant's leaf area (increasing yields focuses on managing a plant’s genotypic plasticity); and (2) the use of eFit based on tNpp (increasing productivity focuses on managing a plant’s phenotypic plasticity). The former framework focuses on photosynthetic efficiency while the second focuses on a site-scale index of a plant’s total productive capacity (i.e., biomass), not its yields, which varies under different soils and micro-climatic conditions [ 35 , 46 ].

Country-level (large-scale) and high-resolution (small-scale) localized data can be used to explore the utility of both frameworks since India experiences a high frequency of droughts that decrease crop yields. Goparaju and Ahmad [ 72 ] included a map of India showing the decadal (2005 to 2014) precipitation deficit, suggesting that the entire country experiences droughts that will impact crop growth and yields. The extensive areal coverage of droughts in India indicates the importance of having a framework that allows crops to be selected based on the diversity of soil and climatic conditions found at the site level. Since droughts in India occur throughout most of the country, there is a need to be able to select crops that are fine-tuned to India’s agricultural landscape at the plot level. Plants also need to adapt and grow under stochastic drought conditions in a diversity of edaphic and climatic conditions. When a farmer cannot manage their crops by irrigation and fertilizer applications, they need to be able to select crop plants capable of growing in their specific soil and micro-climate and capable of adapting to short-term changes in edaphic conditions.

Here, the variation in the TNpp max based on the light-use-efficiency model did not explain the significant differences in tNpp of crops on the dominant soil types (UN FAO), it suggests that a light-use-efficiency model is not sensitive to measuring changes in crop yields at the small-to-meso scale. This research used TNpp max , a surrogate to the light-use-efficiency model, across the different ecological and soil zones of India’s major commercial crop regions and found no significant relationships between TNpp max and the dominant soil types as well as temperature and precipitation. This model is not sensitive to identifying how a crop adapts to its changing environment mechanistically [ 73 ]. Also, it does not factor in a crop’s ecophysiological and evolutionary adaptations at the site scale and could not identify climatic conditions as setting growth thresholds [ 46 ].

Photosynthetic rates of a plant have been essential variables to monitor to determine the amount of carbon a plant can fix and how much biomass is produced based on the limits to growth at the site level. Despite multispectral images being used to predict the yields of Zea mays to assess when crops need to be irrigated, nutrients applied, and when insects or disease organisms attack them [ 74 ], it does not allow you to determine if a plant can still adapt to the changing environmental conditions occurring with climate variability. Multispectral images work as part of management because they focus on changes occurring at the leaf level but not on the plant holistically as an organism responding to its environment. This means that increasing the photosynthetic efficiency is important, but relying on it exclusively would ignore how the site factors limit the ability of a plant to increase growth.

The second approach is eFit, i.e., which is based on tNpp and focuses on plant phenotypic plasticity. This model focuses on the impact of site edaphic and micro-climatic conditions on crop growth and selecting plants better adapted to local site growth conditions. This framework was sensitive to the dominant UN FAO soil types and identified three soils types (Cambisols, Luvisols, and Nitosols) that had a significantly higher upper threshold of tNpp of 19.9, 21.3, and 25.4 Mg ha -1 yr -1 , respectively. This suggests that these soil types support high growth rates. Except for Nitosols, some sites were also in the low and medium tNpp clusters, suggesting that these sites have other site-limiting growth factors that reduce the potential achievable productivity at these sites. Interestingly, each dominant soil type was represented in the three tNpp clusters except for the Nitosols and the Xerosols. It would be worth focusing on each of the tNpp clusters and researching each site to determine what factors placed some sites in the low and medium cluster groups.

The highest tNpp levels were recorded in the Nitosol soil type, which is also expected since these are well-drained, deep soils with a clayey subsurface horizon [ 75 ]. Nitosols are also soils where deep-rooting crops should be planted so crops can access deeper sources of water and nutrients, allowing higher resilience under drought conditions. Other soil types had lower or intermediate tNpp levels, suggesting that management could mitigate multiple site-level factors that limit growth. For example, Xerosols were devoid of crops growing in the high tNpp cluster, which is expected considering these are desert soils with low organic matter and N levels and need to be irrigated to be productive [ 75 ]. Also, Fluvisols and Vertisols had significantly lower tNpp; Fluvisols are very young soils and need to be irrigated to grow crops, whereas Vertisols shrink and swell depending on moisture levels. Further assessment of the low, medium, and high tNpp clusters is worthwhile pursuing to determine why there are statistically significant clusters of low productivity. This could help managers determine what crops to grow on their land based on factors limiting growth.

The high frequency of drought helps to explain the low eFit values by dominant soil types (UN FAO) in croplands. Compared to the eFit values of 14% for rice and 19% for maize calculated by Gordon et al. [ 60 ], the values for India varied from 0.3 to 8.9%, suggesting that some soil types produced higher biomass, but there are many sites at the lower end of eFit percentages. The large variation in eFit by soil type also shows that other factors limit the growth of crops that need to be evaluated. The highest tNpp and eFit were recorded in UN FAO’s Nitosols soil type. This justifies increasing management practices to focus on ameliorating poor soil health and planting crops with sufficient allocation to roots to improve crop yields.

We identified areas that need further research to determine whether the underlying soil factors can be managed to improve the potential productivity of agricultural lands. Further exploration of the relationships between soils and productivity would need to use the soil types (UN FAO classification scheme) to explore how to increase the productive capacity of these sites. The three dominant agricultural soil types (Alluvial, Red/Yellow, and Black soil types also used by the Indian Council of Agricultural Research) [ 70 ] were not sensitive in identifying the ranges of tNpp in each soil type due to their generality and aggregated nature of encompassing several UN FAO soil types. This made them less useful for selecting plants to grow at a site, especially since similar crop plants were grown in each soil type [ 70 ].

This research developed a framework that can be an early warning indicator that plant growth rates may decrease due to the most limiting resources, e.g., rainfall. In contrast, the photosynthetic-use-efficiency model did not indicate the source-sink relationships and, thus, how a plant phenotypically adapts to its environment by shifting allocation between defense, nutrient uptake, and fixing carbon [ 46 ]. This is where determining the eFit [ 63 ] shows promise as a framework to measure the growth yields at the site-specific scale. It will assess how much the site edaphic and micro-climatic conditions limit a plant from reaching its productive capacity, i.e., how close a plant can grow to its theoretical maximum potential productivity based on the growth limiting resources. Gordon [ 60 ] wrote about the need to manage where you are to achieve ecosystem management at the site level. Sun et al. [ 76 ] reported that an integrated measure of soil and leaf physiological factors was most indicative of crop yields. Also, they reported that soil organic matter levels and metabolic enzymes, e.g., invertase, sucrose synthase, were the dominant factors that affected the yields of banana plants. This work supports the need for a more integrative approach to assess the limits to crop yields and why developing an eFit and crop productive capacity at the site level is warranted and needed.

In some situations, with interest in developing sustainable agricultural practices, alternative approaches that ameliorate the soil organic matter levels or interplanting trees/shrubs with crop plants will need to be explored [ 77 – 79 ]. This would approach agriculture from the angle of remediation of the edaphic environment to increase its retention or water-holding capacity when climate change results in decreased precipitation levels, as shown by Goparaju and Ahmad [ 72 ] for India’s major grain production areas. They called for a diversified approach to address climate change impacts and better-diversified farm output [ 80 ]. These are important factors that need to be addressed. Still, we would suggest that there needs to be a better approach to selecting plants to grow in different parts of India (and other places around the world), considering that drought frequencies are high. A high proportion of India’s agriculture experiences droughts as shown by Goparaju and Ahmad [ 72 ], with 54% of India’s total land area experiencing high or extremely high-water stress.

A future experiment is needed that combines the first phase of this research as tools to see ‘how plants are doing’ and then measure the productive capacity of a crop planted across the latitudinal and ecoregions of India. These data could then be used to develop a framework that could inform a decision tree to determine what plants to grow in different soils in India. It would combine phenotypic and genotypic factors to select plants for different sites and determine an eFit for each crop. This is especially important with the climate change impacts we are experiencing since the traditional approaches to selecting and managing crops may be less suitable and less flexible. Today, the climatic conditions are different, and their impacts vary based on the edaphic conditions of a local site.

Since most plants do not reach their genetic growth potential, a holistic approach is needed to assess plant growth potential to identify site-scale growth-limiting factors. This would include a plant’s photosynthetic potential but should also include improved adaptation at the root level to select more suitable crops to grow [ 45 ]. This is based on how site-level edaphic and climatic factors constrain a plant’s productive capacity. Suppose these crop growth limiting factors cannot be alleviated, e.g., soils with low water holding capacity or low nutrients [ 69 ], and thus have a lower potential to achieve higher total yields. In that case, lower crop yields are possible, and crops that are better adapted to the soil and climatic conditions at the site scale should be selected for cultivation. This is because plants allocate carbon to tissues and organs that acquire the scarce resources needed for its growth. When this does not happen, crop yields of the product being grown may decrease, as a plant may allocate more to roots to acquire the nutrients deficient in the soil or grow deeper roots during drought conditions.

Understanding how plants adapt to a dynamic climate is required in order to compensate for site constraints that modulate growth rates. This would require developing a unique site-specific internal reference that combines knowledge of the plant growth rates and their adaptive capacity to dynamic growth environments. The goal is to achieve higher growth rates at the site by guiding the allocation of energy to the desired plant parts. The internal reference of productivity potential represents the energy available for the response of phenotypic adaptation to a diversity of soil and climatic conditions and how each plant’s adaptive capacity interacts with its growth environment [ 35 , 69 ].

Intensive farm management practices will continue to be utilized since larger-sized farms (> 50 ha in size) accounted for more than 70% of the world’s farmland area in 2010 [ 81 ]. The problem with larger farm sizes growing the same crop is that planting genetically similar or identical varieties of crop plants means that it will increase the area of crop growth but does not allow for plants to phenotypically adapt to the wider range of soil conditions that it will experience under the stochastic changes in growth conditions. If the average size of a farm continues to increase globally, technological developments will be essential to efficiently and economically manage and harvest farm fields due to farm labor shortages [ 82 ]. This has driven the focus on increasing crop yields by increasing photosynthetic efficiency [ 83 – 84 ]. The source-sink relationships, however, suggest that as a crop genotype selected mainly for an increased photosynthetic efficiency to increase crop yields might not be adapted to mitigate future resource-limiting factors, such as water or nutrients, as those selected genotypes may be less able to allocate carbon for increased root productivity [ 35 – 36 ]. Research by Banerjee [ 85 ] found that foliar plasticity is not part of the adaptive capacity of a crop to its environment and that genetically selecting a crop for its greater drought tolerance reduced the biomass growth of Phaseolus vulgaris L. As more carbon is shifted to a plant component, allowing it to acquire one of the limiting growth resources, there then becomes insufficient carbon to allocate to other plant parts that enable a response to other growth limiting factors, i.e., evolutionary tradeoffs.

The caveat of evolutionary tradeoffs is that there is no increase of functionality, nor increase of energetic efficiency of one part of a biological system that does not require compensation of another, i.e., there are inherent mechanisms that manage the process. Tradeoffs manifest at every level of biological organization (i.e., cellular to organismal to ecological) and arise because individual traits that we wish to promote in crops are imbedded within complex integrated systems of traits that make up whole organisms.

In terms of evolutionary biology, tradeoffs are the process through which a trait increases the fitness of an organism. Tradeoffs are integral to life because there are always competing demands for limited resources that drive the response to constraints, and this process is why organisms optimize their adaptive ecophysiology. Tradeoffs among crop plants generally can be defined in several non-mutually exclusive terms [ 86 ]:

• allocation constraints caused by a limited resource, such as increasing allocation to roots instead of leaves under drought conditions,
• functional conflicts, where an enhanced performance of traits for higher yields decreases nutritional value, tolerance of high temperatures, or resistance to pests;
• shared biochemical pathways arise from highly conserved molecular pathways that are shared between different traits, some that benefit fitness (e.g., survival, reproduction, fecundity) and some that are detrimental to fitness;
• antagonistic pleiotropy, where one gene controls more than one trait. For example, a gene is selected as it is beneficial for reproduction in early life stages, but also codes for accelerated aging, co-selecting for senescence;
• growth-defense/ecological interactions, herbivory triggering an increased production of secondary metabolites or immobilization of sugars to dissuade pests, thereby taking resources from reproduction or growth. Also, every plant−pathogen-pest system is unique, and management will need to understand the tradeoffs with a particular crop or field condition; and,
• abiotic stressors, such as conservative stomatal behavior in terms of water loss per unit carbon gain under warming and drought conditions. We should expect complex linkages between different tradeoffs, e.g., root hydraulics/root length affecting stomatal physiological and leaf cooling mechanisms linked to aerodynamic leaf resistance and evaporative cooling.

Tradeoffs are most common when energetic or nutrient challenges are extreme. The plant types that persist in harsh conditions are usually found in the tail ends of phenotypic distributions. Tradeoffs are also embedded within a temporal framework where the compensatory mechanisms may operate over very different time scales; from immediate to evolutionary.

Crop plants respond to changing climatic regimes and variable site conditions via physiological changes that enable their localized adaptation. All responses draw from available energy and nutrient pools, so trade-offs must be prioritized. Crops will need adaptive flexibility as the demand and complexity of these switches increase; this will influence crop success (however it is measured) in responding to variability in environmental conditions. Consideration must also account for soil-microbial-mycorrhizal-plant interactions and the tradeoffs required to maintain symbiotic associations. All crops cope with pests, pathogens, and physical damage from weather, including wind, drought, flooding, and temperature extremes that occur outside the safe operating space for a species or variety. Crops inevitably make “decisions” about when, where, and how to allocate their available resources, ultimately determining yields.

The existing paradigm must be overhauled when farming strategies or management cannot produce reliable crops year-over-year. Climate variability will require flexibility in the selection, cultivation, and expectation of plant performance, which realistically accounts for all the variables impacting yields. As we cope with our changing environment and work to foster and utilize a plant’s innate adaptive capacity to respond to increasingly dynamic stressors, an approach that balances available resources will fully benefit from plant genetic diversity. Farmers need to select crop plants capable of growing in specific soil and micro-climate niches and that also possess the traits to adapt to short-term changes in environmental conditions. Plants that are specialists in adapting to site-level conditions must also be generalists adapting to unpredictable temperature and precipitation regimes. A sort of “surgical precision” farming that can respond to small-scale characteristics across the “grow landscape” that may change every season and can produce sufficient and reliable yields to warrant the effort.

Trade-offs will be a necessity along with compromises that may result in moderate returns that take time to build to a better-than-average yield, i.e., avoiding boom and bust cycles associated with drought with conventional crop breeds or genetically modified varieties. This will re-establish crop resilience and reliability and regain the knowledge of experience lost over the last one hundred years since the green revolution and the advent of industrial-scale mono-crop farming. There is a new urgency to understand how best to exploit the adaptive traits heritage varieties possess while also increasing the diversity of crops, identifying new beneficial traits, and re-establishing lost or endangered strains adapted to niche characteristics of the agricultural diversity of India.

Understanding how crops will respond to changing climatic conditions in all dimensions is the core of agroecology. In the face of rapid environmental change natural populations avoid extinction via two evolutionary mechanisms; phenotypic plasticity and/or adaptive evolution [ 87 ]. How the interplay between these evolutionary processes and changing environmental selective pressures will unfold remains less clear. We need to better understand the levels at which existing crop genetic diversity and phenotypic plasticity help or hinder adaptive capacity at the site level, and our study provides a promising path to explore this avenue, as we search for ways to incorporate sustainable crop management while still meeting our food and energy demands.

Materials and Methods

Site description and droughts.

India was selected as the case study because it’s where the original Green Revolution emerged to address inadequate agricultural productivity to feed the country’s rapidly growing population. India is an Agrarian society and emerged as the most populous nation in April 2023 [ 88 ]. India also has a rich history of research and data on its soil types and climate and is experiencing a loss of crops due to droughts [ 72 ]. India has experienced many droughts and an increasing frequency of significant droughts over the last 20 years compared to an average year [ 89 ]. A report on the meteorological history of droughts recorded the following pattern [ 89 – 90 ]:

• During 1871–2015 , there were 25 major drought years , defined as years with All India Summer Monsoon Rainfall (AISMR) less than one standard deviation below the mean (i . e ., anomaly below percent) : 1873 , 1877 , 1899 , 1901 , 1904 , 1905 , 1911 , 1918 , 1920 , 194 1 , 1951 , 1965 , 1966 , 1968 , 1974 , 1979 , 1982 , 1985 , 1986 , 1987 , 2002 , 2009 , 2014 and 2015 .
• The frequency of drought has varied over the decades . From 1899 to 1920 , there were seven drought years . The incidence of drought came down between 1941 and 1965 when the country witnessed just three drought years .
• However , during the 21 years , between 1965 and 1987 , there were 10 drought years which was attributed to the El Nino Southern Oscillation (ENSO) . Among the many drought events since Independence , the one in 1987 was one of the worst , with an overall rainfall deficiency of 19% which affected 59–60% of the normal cropped area and a population of 285 million . This was repeated in 2002 when the overall rainfall deficiency country as a whole was 19% .

These droughts are reducing crop yields but at different rates across its agricultural landscapes. India’s agricultural areas experienced either low or high drought frequency between 2000 and 2019 [ 89 ]. Agricultural productivity decreased by 40% where farming is dependent on precipitation. The worst drought year in India was 1987, when rainfall was 75% below normal. Kumar [ 91 ] reported rainfall was less than 50% of the average, and food grain output productivity of the yield dropped by 20%. Also, Kumar [ 91 ] reported that between 1978 and 1983, lands that were not irrigated had a 30–50% decline in yields, but even irrigated lands experienced decreased yields of 10–20%. The food grain productivity decreased by 29 million tons from an expected 90 million metric tons for this year [ 91 ].

Data, study design and analysis

This paper used 356 country-wide sites with administrative-level climatic and soil data to demonstrate eFit and tNpp’s utility in developing an internal site standard. We filtered the data to the land cover classification of croplands west of 90° E longitude, with a grow season temperature above 19° C and a sample size greater than eight ( n > 8) in each dominant soil type. We explored how much growth-limiting factors decrease tNpp, identified the potential productivity at the site level, and the potential for managers to increase actual productivity.

This study was designed to compare two assessment frameworks to identify sensitivity to site-level factors and which variables determine the potential yields and productivity of a crop at a site level: (1) the light-use-efficiency model (or carbon-centric approach); and (2) the use of eFit based on total tNpp. The second framework incorporates the first framework and expands the assessment to be sensitive to site-level limitations on crop growth by eco-region and dominant soil types in India. The framework aims to estimate a crop’s TNpp max and actual productive capacity based on local edaphic and climatic conditions under which plants actually grow.

This research explored whether a plant’s photosynthetic potential will be sufficient to determine its productive potential and whether it can link the site-level limits of soils and climates. This framework uses ‘total’ crop productivity, not just the ‘product’ biomass harvested. It provides methods to estimate the maximum photosynthetic potential of a site and the actual productivity to determine which cultivars should be planted at a given site, especially focusing on the soil and climatic characteristics by knowing how the crop may adapt to its site.

Within a GIS we mapped the primary boundary of India to constrain the spatial extent of our analysis ( https://map.igismap.com/gis-data/india/administrative_outline_boundary ) and level-two administrative divisions (which includes districts and is part of the Global Administrative Areas 2015 (v2.8) dataset). Reference sites consisting of spatial datapoints were developed by calculating the centroid of each second-level administrative boundary division, including districts, that is part of the Global Administrative Areas 2015 (v2.8) dataset ( https://gadm.org/ ).

Prior to further analysis, the northeast region of India consisting of the eight states of Assam, Arunachal Pradesh, Manipur, Meghalaya, Mizoram, Nagaland, Tripura, and Sikkim were removed from the data set as the landscape is dominated by tropical, subtropical, and temperate broadleaf and mixed forests where there is limited agricultural development. Through a series of steps within a geographic information system (GIS), a diversity of data types and sources were gathered and assigned to each site with overlay and spatial relationship functions.

Environmental data included ecoregion classification from the Terrestrial Ecoregion GIS data portal ( https://www.gislounge.com/terrestrial-ecoregions-gis-data/ ) which depicts 846 described ecoregions across the planet. Ecoregions are ecosystems of regional extent, color-coded to highlight their distribution and the biological diversity they represent with the goal of E. O. Wilson’s “Nature Needs Half” initiative to protect half of all the land on Earth to save a living terrestrial biosphere ( https://ecoregions.appspot.com/ ).

Major soil types were downloaded from the FAO soil survey portal ( https://www.fao.org/soils-portal/soil-survey/ ), and land cover classifications, and other climatic and environmental variables consisting of vector and raster data were then spatially joined or summarized to the reference sites for further processing [ 92 ]. We included only sites where land cover was classified as “cropland,” which served as the locations to calculate TNpp max , total productivity, and eFit estimates.

Digital Soil Map for India

India is a very diverse landscape composed of 39 level IV ecoregions (21 used in this study) and 10 biomes (5 used in this study). Six dominant soil types were associated with the reference sites, and an additional ten soil types represented the total diversity of soil orders found in India ( Fig 12 ).

Country-wide cropland reference sites (dark points) based on the centroid of administrative units (n = 356). Level IV ecoregions are encompassed within the biomes outlined in yellow and listed by number on the map and described in the legend. The colors indicate the dominant soil types used in this study, and the additional diversity of soil types not covered by a reference site are depicted as dark grey. Data sources for the base map as follows: Boundary of India layer is part of the Global Administrative Areas 2015 (v2.8) dataset. Hijmans, R. and University of California, Berkeley, Museum of Vertebrate Zoology. (2015). Boundary, India, 2015. UC Berkeley, Museum of Vertebrate Zoology. Available at: http://purl.stanford.edu/jm149wc6691 ; Soil vector data based on the FAO-UNESCO Soil Map of the World available at https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/446ed430-8383-11db-b9b2-000d939bc5d8 ; Ecoregion layer is licensed under CC-BY-4.0 and available at Ecoregions 2017 © Resolve https://ecoregions.appspot.com ; Administrative boundaries are derived from OpenStreetMap data licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) and available at GIS Data | MAPOG. There were no changes made to the base layers.

https://doi.org/10.1371/journal.pstr.0000122.g012

The characteristics of the three dominant soil types used in this study from the ICAR classification scheme are described in Table 4 .

https://doi.org/10.1371/journal.pstr.0000122.t004

Climatic Variables

The climatic variables were annual mean, minimum and maximum air temperatures, and total yearly precipitation as continuous data to tease out critical environmental thresholds influencing the growth of crops. In addition, average monthly temperature data and average monthly solar radiation data [ 93 ] were downloaded and extracted at the geographic coordinates of each reference site [ https://www.worldclim.org/data/bioclim.html ] at a spatial resolution of 30 seconds (~1 km 2 ). The summary data calculated for each site were: (i) the length of the growing season (i.e., days when the temperature exceeded zero), and (ii) the mean monthly temperature days for the growing season.

We evaluated the underlying structure of TNpp max and grow temperature with unsupervised clustering analysis to identify threshold effects. We computed all pairwise dissimilarities (distances) between observations based on a Euclidian metric with determination of the medoid by a robust partitioning method (PAM). The optimal number of clusters was selected by evaluating the average silhouette width and the variance explained by the first two principal components with the R package “cluster”. For the data analyses, precipitation thresholds were grouped into four classification levels described in Vogt et al. [ 69 ].

Calculation of Maximum Potential Productivity

To estimate crops’ maximum potential productivity (TNpp max ), a modified Loomis-Williams model initially developed for crops in the 1960s was used [ 61 – 62 ]. Their research supports using photosynthesis as each plant’s assimilation framework and explores site limitations on plant productive capacity [ 61 , 63 ]. A crop’s photosynthetic capacity is limited by the resources constraining its ability to fix carbon, such as its ability to acquire growth-limiting resources at the site level, e.g., its edaphic and micro-climatic conditions [ 69 ].

The specific theoretical maximum productive capacity potential was estimated using a light-use efficiency model, based on the amount of solar radiation available during a growing season at each site and its plant physiological parameters [ 35 , 93 ]. The light-use-efficiency model is calculated as a product of solar radiation, light-interception efficiency ( Ε i ), the efficiency at which canopies absorb solar radiation, and the conversion efficiency ( Ε c ), or the rate at which solar radiation is absorbed by C3 plants and is converted into biomass [ 35 , 62 , 94 ].

Calculation of Ecosystem Fit

Ecosystem Fit (eFit) is the proportion of productive capacity of the site that can be improved upon using as an index the upper site-level maximum threshold of total net primary productivity capacity for the site based on the site’s limitations to growth [ 63 ]. In this calculation, Net Primary Productivity (tNpp) integrates green plant functions and includes changes in carbon allocation shifts in the source-sink relationships [ 46 ].

The eFit model was developed for forests. It produces an internal site reference productivity level or an index of the site-level potential productive capacity to a plant’s ecophysiological and evolutionary functions, site-level edaphic conditions, and climatic constraints [ 62 – 63 ]. This indexing approach has not been previously used in agriculture to assess the eFit of a crop to local site growth-limiting conditions. Ecosystem fit cannot be currently calculated using field-collected data for crops in this study because of the lack of a robust database populated by data on a diversity of crop source-sink relationships to seeds, fruit, aboveground plant growth, root biomass, and secondary defensive chemicals. The methodology to calculate eFit is described in Klock et al. [ 62 ].

Statistical methods and calculations

To understand the underlying structure and threshold effects of the data, we applied an unsupervised (all identifying classifiers removed) clustering method to TNpp max , tNpp, and the climatic variables of temperature and precipitation. Clustering reveals the natural groupings inherent in the data and provides an empirical estimation of the thresholds that inform how to characterize the vegetation response across the landscape.

We hypothesized that soil conditions would inform primary productivity. Therefore, we tested for a threshold effect by computing all pairwise dissimilarities (distances) between observations based on a Euclidian metric with determining the medoid by a robust partitioning and aggregation around medoids (PAM). The medoid represents the stabilized median of the clustered data. The optimal number of clusters was selected by evaluating the average silhouette width and the variance explained by the first two principal components with the R package “cluster” [ 95 ]. The cluster analysis identified two relatively homogenous clusters for TNpp max and three clusters for tNpp, thus data summaries were classified by these groups, referred to as low, medium, or high (Tables 2 and 3 ).

A series of parametric and non-parametric approaches were explored to understand the association between productivity metrics (e.g., TNpp max , tNpp, eFit), and dominant soil groups and climatic variables. All continuous responses were evaluated for meeting the assumptions of normality (if applicable), and to reduce experiment-wise error rates (Type I error, falsely rejecting the NULL), we adopted a more stringent level of significance for all pairwise comparisons with Bonferroni correction to further control for the probability of committing a Type I error [ 96 ]. We tested for homogeneity of variance with a Bartlett test. The responses of TNpp max , tNpp, and eFit were normally distributed and mostly balanced (assumption of Gaussian distribution) and we used Welch F tests to evaluate the relationships to the dominant soil groups. The Welch test is based on the weighted means of each group (sample size and variance) and the grand mean based on the weights mean of each group for the sum of squares. These tests provided the general benefit of high power and low probability of Type I error. We report the grand mean value in figures to represent that value against which the class means were evaluated. We used partial Omega squared as a measure of effect size as it is widely viewed as a lesser biased alternative when sample sizes are small (e.g., a partial effect size [ω2 = 0.25] would indicate that 25% of the variance was explained by the predictor). We used the standardized effect size to represent the practical significance of our results, instead of relying on statistical thresholds, and to make comparisons among very different responses. We also tested for statistical power of each comparison. All statistical tests were based on (α = .05), with analysis conducted in the programming language R ver. 4.2.2 [ 97 ] and geospatial data processed using ESRI ArcDesktop ver 10.8 [ 98 ].

Supporting information

S1 file. outline of the framework for data processing with chunks of embedded r code..

https://doi.org/10.1371/journal.pstr.0000122.s001

S1 Data. Processed dataset for analysis.

https://doi.org/10.1371/journal.pstr.0000122.s002

• 1. FAO, IFAD, UNICEF, WFP and WHO. The State of Food Security and Nutrition in the World 2023. Urbanization, agrifood systems transformation and healthy diets across the rural—urban continuum. Rome, FAO. 2023. https://doi.org/10.4060/cc3017en .
• 2. United Nations Department of Economic and Social Affairs, Population Division (2022). World Population Prospects 2022: Summary of Results. UN DESA/POP/2022/TR/NO. 3. https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/wpp2022_summary_of_results.pdf .
• 3. World Bank. World Development Report 2008. January 2007. https://doi.org/10.1596/978-0-8213-6807-7 .
• View Article
• Google Scholar
• PubMed/NCBI
• 6. Ritchie H, Roser M, Rosado P. Crop Yields. Published online at OurWorldInData . org . 2022. https://ourworldindata.org/crop-yields ’ [Online Resource].
• 19. Gutschick VP. Photosynthesis, Growth Rate, and Biomass Allocation. In Ecology in Agriculture. 1997, 39–78. (Jackson LE, ed) Elsevier Inc. https://doi.org/10.1016/B978-0-12-378260-1.X5000-3
• 25. Fageria NK, Balgar VC, Jones CA. Growth and Mineral Nutrition of Field Crops. 3 rd Ed. Boca Raton (FL): CRC Press, Taylor & Francis Group. 2010. ISBN-10. 0824700899. 640 pp.
• 55. Vogt KA, Asbjornsen H, Ercelawn A, Montagnini F, Valdes M. Chapter 8. Ecosystem integration of roots and mycorrhizas in plantations. 1997; 247–296. In: Nambiar, S., A. Brown, eds. Management of Soil , Water and Nutrients in Tropical Plantation Forests . ACIAR: Australia. https://www.aciar.gov.au/sites/default/files/legacy/node/2204/mn43_pdf_55201.pdf .
• 57. Geiger DR, Servaites JC. Carbon allocation and response to stress. In: Mooney H, Winner W, E. Pell E, Chu E, eds. pp. 103–127. San Diego, New York, Boston: Academic press. 1991. https://ecommons.udayton.edu/bio_fac_pub/42 .
• 60. Gordon JC, Bormann BT, Kiester AR. The physiology and genetics of ecosystems: A new target or Forestry contemplates an entangled bank. Proceedings of the 12th North American Forest Biology Workshop. Sault Ste. Marie, Ontario, Canada. Aug 17–20, 1992. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/file:///C:/Users/kvogt/Downloads/GordonNAFBW1992Procecofit%20(6).pdf.
• 63. Gordon JC, Bormann BT, Jacobs L. The concept of ecosystem fit and its potential role in forest management: a primary research challenge. Proceedings of the sessions S4.02 and S4.12 of the IUFRO XX World Congress, 6–12 August 1995, Tampere, Finland. https://docplayer.net/142699633-The-concept-of-ecosystem-fit-and-its-potential-role-in-forest-management-a-primary-research-challenge.html .
• 64. Hayes A. Crop Yield Definition, Formula, Statistics. Investopedia. 11 July 2022. Link: https://www.investopedia.com/terms/c/crop-yield.asp#:~:text=Crop%20yield%20is%20a%20standard,per%20acre%20in%20the%20U.S .
• 65. Mulvaney MJ, Devkota P. Adjusting crop yield to a standard moisture content. SS-AGR-443, UF/IFAS Extension 2020. https://edis.ifas.ufl.edu/publication/AG442 .
• 68. Cohen J. 1988. Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers.
• 70. ICAR (Indian Council of Agricultural Research), Research Series, Issue 19. 1953. Indian Council of Agricultural Research, 1953. 112 pp. https://books.google.com/books/about/I_C_A_R_Research_Series.html?id=lukuAAAAMAAJ .
• 75. FAO and ITPS. Status of the World’s Soil Resources (SWSR)–Main Report. Food and Agriculture Organization of the United Nations and Intergovernmental Technical Panel on Soils. 2015. Rome, Italy. https://www.fao.org/3/i5199e/i5199e.pdf .
• 77. Middleton N, Thomas D. World Atlas of Desertification. 1992; United Nations Environment Programme. UNEP Publications: London. 69 pp. https://wedocs.unep.org/20.500.11822/42137 .
• 80. EPA. Climate Impacts on Agriculture and Food Supply. 2022. Link: https://climatechange.chicago.gov/climate-impacts/climate-impacts-agriculture-and-food-supply .
• 85. Banerjee A. Crop yields: Adaptation to site-level edaphic and climate limitations to growth [dissertation]. Seattle (WA): University of Washington, 2023.
• 88. Hertog S, Gerland P, Wilmoth J. India overtakes China as the world’s most populous country. UN DESA Policy Brief No. 153, 24 April 2023. Link: https://www.un.org/development/desa/dpad/publication/un-desa-policy-brief-no-153-india-overtakes-china-as-the-worlds-most-populous-country/ .
• 89. Department of Agriculture and Farmers Welfare. Manual for Drought Management December 2016 (updated upto December 2020). 2020. Government of India. New Delhi. https://agriwelfare.gov.in/Documents/Updated%20Drought%20Manual_0.pdf .
• 90. Samra JS. Review and analysis of drought monitoring, declaration and management in India. Working Paper 84. Drought Series Paper 2. 2004; Colombo, Sri Lanka: International Water Management Institute. https://www.preventionweb.net/files/1868_VL102135.pdf .
• 92. FAO Map Catalog. Digital Soil Map of the World. FAO soil portals. 2023. https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/446ed430-8383-11db-b9b2-000d939bc5d8 ; https://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/ . Fick SE, Hijmans RJ. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology. 2017; 37(12), pp. 4302–4315.
• 95. Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., Hornik, K., 2019. cluster: Cluster Analysis Basics and Extensions. R package version 2.1.2—See the ’Changelog’ file (in the package source). https://CRAN.R-project.org/package=cluster .
• 97. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, 2022. Vienna, Austria. URL https://www.R-project.org/ .
• 98. ESRI. ESRI Arc Desktop version 10.8. 11/24/2020. ISBN– 9781589486140.

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GIS, remote sensing, and analytical hierarchy process (AHP) approach for rainwater harvesting site selection in arid regions: Feija Plain case study, Zagora (Morocco)

• Published: 20 September 2024

Cite this article

• Adil Moumane 1 ,
• Abdelhaq Ait Enajar 2 ,
• Fatima Ezzahra El Ghazali 3 ,
• Abdellah Khouz 4 ,
• Ahmed Karmaoui 5 ,
• Jamal Al Karkouri 1 &
• Mouhcine Batchi 1  

The watermelon cultivation industry in Morocco's arid desert regions has experienced swift expansion due to increasing demand both nationally and globally. Nevertheless, this growth has led to the depletion of the already scarce groundwater resources, necessitating a paradigm shift in water resource management. This study adopts an integrated approach, leveraging field measurements, laser diffraction for soil particle size analysis, GIS mapping, and remote sensing, to pinpoint optimal sites for rainwater harvesting (RWH). A comprehensive methodology involving Soil Conservation Service Curve Number (SCS CN), and various conditioning criteria layers (Rainfall, Land Use and Land Cover, Geomorphology, Slope, Topographic Wetness Index, Infiltration number, and Aspect) was applied. The Analytic Hierarchy Process (AHP) assigned weights to criteria, and a Weighted Linear Combination (WLC) approach in GIS produced an RWH suitability map. The map, classified into four zones (unsuitable, low, moderate, and high cover), showed promising potential for 5.24% of the study area. Field data validation after significant rain events confirmed an 86 percent overall map accuracy. Eight recommended RWH sites, including GPS coordinates, are proposed for decision-makers to facilitate strategic implementation, ensuring sustainable water availability for both drinking and irrigation in this arid region.

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ABHDON (2021) Etude d’élaboration du contrat de nappe de Feija. Mission 2: Etat des lieux, tendance de l’évolution future et identification d’axe stratégiques d’amélioration de la situation actuelle. Rapport de synthèse. Agence du bassin hydraulique de Draa Oued Noun

Aboubakr EZ, Abdelhalim T, Ahmed A et al (2021) Hydrogeological Synthesis of Groundwater Resources: Case of the Feija Watershed (South-east of Morocco). Jeas 2:85–94. https://doi.org/10.32996/jeas.2021.2.1.9

Article   Google Scholar  

Ali S, Ghosh NC, Singh R (2010) Rainfall–runoff simulation using a normalized antecedent precipitation index. Hydrol Sci J 55:266–274. https://doi.org/10.1080/02626660903546175

Article   CAS   Google Scholar  

Al-Talbi lhalm (2019) Water shortage in the Maghreb: Morocco’s thirst revolution.  https://www.goethe.de/prj/ruy/en/dos/wil/21718884.html . Accessed 10 Jul 2022

Aly MM, Sakr SA, Zayed MSM (2022) Selection of the optimum locations for rainwater harvesting in arid regions using WMS and remote sensing. Case Study: Wadi Hodein Basin, Red Sea. Egypt Alexandria Eng J 61:9795–9810. https://doi.org/10.1016/j.aej.2022.02.046

Ammar A, Riksen M, Ouessar M, Ritsema C (2016) Identification of suitable sites for rainwater harvesting structures in arid and semi-arid regions: A review. Int Soil Water Conserv Res 4:108–120. https://doi.org/10.1016/j.iswcr.2016.03.001

Assefa S, Biazin B, Muluneh A et al (2016) Rainwater harvesting for supplemental irrigation of onions in the southern dry lands of Ethiopia. Agric Water Manag 178:325–334. https://doi.org/10.1016/j.agwat.2016.10.012

Bennie J, Huntley B, Wiltshire A et al (2008) Slope, aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland. Ecol Model 216:47–59. https://doi.org/10.1016/j.ecolmodel.2008.04.010

Bera A, Mukhopadhyay BP (2023) Identification of suitable sites for surface rainwater harvesting in the drought prone Kumari River basin, India in the context of irrigation water management. J Hydrol 621:129655. https://doi.org/10.1016/j.jhydrol.2023.129655

Berger E, Bossenbroek L, Beermann AJ et al (2021) Social-ecological interactions in the Draa River Basin, southern Morocco: Towards nature conservation and human well-being using the IPBES framework. Sci Total Environ 769:144492. https://doi.org/10.1016/j.scitotenv.2020.144492

Berrada R (2022) Le monde rural est confronté à la pire sécheresse des trente dernières années. In: Médias24.  https://medias24.com/2022/02/10/le-monde-rural-est-en-train-de-subir-la-pire-secheresse-des-30-dernieres-annees/ . Accessed 11 Jul 2022

Chow VT, Maidment DR, Mays LW (1988) Applied hydrology. Louis Paris, McGraw-Hill, New York St

Google Scholar  

Congalton RG, Green K (2019) Assessing the accuracy of remotely sensed data: principles and practices, 3rd edn. CRC Press, Boca Raton London New York

Book   Google Scholar  

Costache R, Pham QB, Sharifi E et al (2019) Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques. Remote Sensing 12:106. https://doi.org/10.3390/rs12010106

Dangar S, Asoka A, Mishra V (2021) Causes and implications of groundwater depletion in India: A review. J Hydrol 596:126103. https://doi.org/10.1016/j.jhydrol.2021.126103

de Sá Silva ACR, Bimbato AM, Balestieri JAP, Vilanova MRN (2022) Exploring environmental, economic and social aspects of rainwater harvesting systems: A review. Sustain Cities Soc 76:103475. https://doi.org/10.1016/j.scs.2021.103475

El Qorchi F, Yacoubi Khebiza M, Omondi OA et al (2023) Analyzing Temporal Patterns of Temperature, Precipitation, and Drought Incidents: A Comprehensive Study of Environmental Trends in the Upper Draa Basin. Morocco Water 15:3906. https://doi.org/10.3390/w15223906

Ettazarini S (2021) GIS-based land suitability assessment for check dam site location, using topography and drainage information: a case study from Morocco. Environ Earth Sci 80:567. https://doi.org/10.1007/s12665-021-09881-3

Ezzeldin M, Konstantinovich SE, Igorevich GI (2022) Determining the suitability of rainwater harvesting for the achievement of sustainable development goals in Wadi Watir, Egypt using GIS techniques. J Environ Manage 313:114990. https://doi.org/10.1016/j.jenvman.2022.114990

Faé GS, Montes F, Bazilevskaya E et al (2019) Making Soil Particle Size Analysis by Laser Diffraction Compatible with Standard Soil Texture Determination Methods. Soil Sci Soc Am j 83:1244–1252. https://doi.org/10.2136/sssaj2018.10.0385

Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80:185–201. https://doi.org/10.1016/S0034-4257(01)00295-4

Garg KK, Akuraju V, Anantha KH et al (2022) Identifying potential zones for rainwater harvesting interventions for sustainable intensification in the semi-arid tropics. Sci Rep 12:3882. https://doi.org/10.1038/s41598-022-07847-4

Ghazali FE, M’rizig Z (2013) L’impact de réalisation barrage Bou- Tious sur la nappe de Feija dans la province de Zagora (Anti-Atlas central, Maroc). Mémoire de stage de fin d’études. Faculté des Sciences et Techniques Marrakech. http://saidi.ma/memoires/elghazalimrizig.pdf . Accessed 10 Mar 2023

Gómez-Monsalve M, Domínguez IC, Yan X et al (2022) Environmental performance of a hybrid rainwater harvesting and greywater reuse system: A case study on a high water consumption household in Colombia. J Clean Prod 345:131125. https://doi.org/10.1016/j.jclepro.2022.131125

Hagos YG, Andualem TG, Mengie MA et al (2022) Suitable dam site identification using GIS-based MCDA: a case study of Chemoga watershed. Ethiopia Appl Water Sci 12:69. https://doi.org/10.1007/s13201-022-01592-9

Haldar S, Majumder A (2022) Identifying Suitable Location for Surface Rainwater Harvesting Using GIS and Analytical Hierarchy Process. Papers Appl Geograph 8:339–356. https://doi.org/10.1080/23754931.2022.2051196

Hamidy N, Alipur H, Nasab SNH et al (2016) Spatial evaluation of appropriate areas to collect runoff using Analytic Hierarchy Process (AHP) and Geographical Information System (GIS) (case study: the catchment “Kasef” in Bardaskan. Model Earth Syst Environ 2:1–11. https://doi.org/10.1007/s40808-016-0230-7

Hashim HQ, Sayl KN (2021) Detection of suitable sites for rainwater harvesting planning in an arid region using geographic information system. Appl Geomat 13:235–248. https://doi.org/10.1007/s12518-020-00342-3

Hindiyeh MY, Matouq M, Eslamian S (2021) Rainwater Harvesting Policy Issues in the MENA Region: Lessons Learned, Challenges, and Sustainable Recommendations. In: Eslamian S (ed) Handbook of Water Harvesting and Conservation, 1st edn. Wiley, pp 457–473

Chapter   Google Scholar  

Horton RE (1945) Erosional development of streams and their drainage basins: hydrophysical approach to quantitative morphology. Geol Soc America Bull 56:275. https://doi.org/10.1130/0016-7606(1945)56[275:EDOSAT]2.0.CO;2

Karmakar M, Ghosh D (2022) A GIS-based approach for identification of optimum runoff harvesting sites and storage estimation: a study from Subarnarekha-Kangsabati Interfluve. Appl Geomat, India. https://doi.org/10.1007/s12518-022-00433-3

Karmaoui A (2019) Drought and desertification in Moroccan Pre-Sahara, Draa valleys: exploring from the perspective of young people. Geoenviron Disasters 6:2. https://doi.org/10.1186/s40677-019-0118-8

Karmaoui A, Minucci G, Messouli M et al (2019) Climate Change Impacts on Water Supply System of the Middle Draa Valley in South Morocco. In: Behnassi M, Pollmann O, Gupta H (eds) Climate Change, Food Security and Natural Resource Management. Springer International Publishing, Cham, pp 163–178

Karmaoui A, El Jaafari S, Chaachouay H, Hajji L (2022) The socio-ecological system of the pre-Sahara zone of Morocco: a conceptual framework to analyse the impact of drought and desertification. GeoJournal 87:4961–4974. https://doi.org/10.1007/s10708-021-10546-8

Karmaoui A, Moumane A (2016) Changes in the Environmental Vulnerability of Oasean System (desert oasis), Pilot Study in Middle Draa Valley, Morocco. Expert Opin Environ Biol 5 https://doi.org/10.4172/2325-9655.1000135

Klose S (2013) Regional hydrogeology and groundwater budget modeling in the arid Middle Drâa Catchment (South-Morocco). Universitäts- und Landesbibliothek Bonn, Thesis

Kumar T, Jhariya DC (2017) Identification of rainwater harvesting sites using SCS-CN methodology, remote sensing and Geographical Information System techniques. Geocarto Int 32:1367–1388. https://doi.org/10.1080/10106049.2016.1213772

Lamqadem AA, Saber H, Pradhan B (2019) Long-Term Monitoring of Transformation from Pastoral to Agricultural Land Use Using Time-Series Landsat Data in the Feija Basin (Southeast Morocco). Earth Syst Environ 3:525–538. https://doi.org/10.1007/s41748-019-00110-3

Landis JR, Koch GG (1977) The Measurement of Observer Agreement for Categorical Data. Biometrics 33:159. https://doi.org/10.2307/2529310

Mahmood K, Qaiser A, Farooq S, Nisa M, un, (2020) RS- and GIS-based modeling for optimum site selection in rain water harvesting system: an SCS-CN approach. Acta Geophys 68:1175–1185. https://doi.org/10.1007/s11600-020-00460-x

Mahmoud SH, Adamowski J, Alazba AA, El-Gindy AM (2016) Rainwater harvesting for the management of agricultural droughts in arid and semi-arid regions. Paddy Water Environ 14:231–246. https://doi.org/10.1007/s10333-015-0493-z

Matomela N, Li T, Ikhumhen HO (2020) Siting of Rainwater Harvesting Potential Sites in Arid or Semi-arid Watersheds Using GIS-based Techniques. Environ Process 7:631–652. https://doi.org/10.1007/s40710-020-00434-7

Mattivi P, Franci F, Lambertini A, Bitelli G (2019) TWI computation: a comparison of different open source GISs. Open Geospatial Data, Softw Stand 4:6. https://doi.org/10.1186/s40965-019-0066-y

Minucci G, Karmaoui A (2017) Exploring the Water-Food-Energy and Climate Nexus: Insights from the Moroccan Draa Valley. In: Colucci A, Magoni M, Menoni S (eds) Peri-Urban Areas and Food-Energy-Water Nexus. Springer International Publishing, Cham, pp 89–97

Monserud RA, Leemans R (1992) Comparing global vegetation maps with the Kappa statistic. Ecol Model 62:275–293. https://doi.org/10.1016/0304-3800(92)90003-W

Moumane A, El Ghazali FE, Al Karkouri J et al (2021) Monitoring spatiotemporal variation of groundwater level and salinity under land use change using integrated field measurements, GIS, geostatistical, and remote-sensing approach: case study of the Feija aquifer, Middle Draa watershed. Moroccan Sahara Environ Monit Assess 193:769. https://doi.org/10.1007/s10661-021-09581-2

Moumane A, Al Karkouri J, Benmansour A et al (2022) Monitoring long-term land use, land cover change, and desertification in the Ternata oasis, Middle Draa Valley, Morocco. Remote Sens Applic Soc Environ 26:100745. https://doi.org/10.1016/j.rsase.2022.100745

Mu E, Pereyra-Rojas M (2017) Understanding the Analytic Hierarchy Process. Practical Decision Making. Springer International Publishing, Cham, pp 7–22

NRCS USDA (2009) National resource conservation service. National engineering handbook, Part 630: Hydrology, Chap. 7, USDA, Washington, D.C. https://damtoolbox.org/wiki/National_Engineering_Handbook:_Chapter_7_-_Hydrologic_Soil_Groups . Accessed 3 Mar 2022

Nunnally S (2018) In Zagora, Morocco, residents never know when water will flow so they leave the taps on. In: USA TODAY.  https://www.usatoday.com/story/news/world/2018/04/05/morocco-water-shortage/465498002/ . Accessed 10 Jul 2022

Ouali L, Hssaisoune M, Kabiri L et al (2022) Mapping of potential sites for rainwater harvesting structures using GIS and MCDM approaches: case study of the Toudgha watershed, Morocco. Euro-Mediterr J Environ Integr 7:49–64. https://doi.org/10.1007/s41207-022-00294-7

Rana VK, Suryanarayana TMV (2020) GIS-based multi criteria decision making method to identify potential runoff storage zones within watershed. Ann GIS 26:149–168. https://doi.org/10.1080/19475683.2020.1733083

Rejani R, Rao KV, Srinivasa Rao CH et al (2017) Identification of Potential Rainwater-Harvesting Sites for the Sustainable Management of a Semi-Arid Watershed: Identification of potential rainwater harvesting sites. Irrig and Drain 66:227–237. https://doi.org/10.1002/ird.2101

Saaty TL (1991) Some Mathematical Concepts of the Analytic Hierarchy Process. Behaviormetrika 18:1–9. https://doi.org/10.2333/bhmk.18.29_1

Saaty TL (2013) The modern science of multicriteria decision making and its practical applications: The AHP/ANP approach. Oper Res 61:1101–1118. https://doi.org/10.1287/opre.2013.1197

Saaty TL, Vargas LG (2012) How to Make a Decision. Models, Methods. Concepts & Applications of the Analytic Hierarchy Process. Springer, US, Boston, MA, pp 1–21

Saha A, Ghosh M, Chandra Pal S (2021) Identifying Suitable Sites for Rainwater Harvesting Structures Using Runoff Model (SCS-CN), Remote Sensing and GIS Techniques in Upper Kangsabati Watershed, West Bengal, India. In: Adhikary PP, Shit PK, Santra P et al (eds) Geostatistics and Geospatial Technologies for Groundwater Resources in India. Springer International Publishing, Cham, pp 119–150

Sarkar S, Biswas S (2022) Application of integrated AHP-entropy model in suitable site selection for rainwater harvesting structures: a case study of upper Kangsabati basin. India Arab J Geosci 15:1684. https://doi.org/10.1007/s12517-022-10958-x

Schilling J, Hertig E, Tramblay Y, Scheffran J (2020) Climate change vulnerability, water resources and social implications in North Africa. Reg Environ Change 20:15. https://doi.org/10.1007/s10113-020-01597-7

Shadmehri Toosi A, Ghasemi Tousi E, Ghassemi SA et al (2020) A multi-criteria decision analysis approach towards efficient rainwater harvesting. J Hydrol 582:124501. https://doi.org/10.1016/j.jhydrol.2019.124501

Silva-Novoa Sánchez LM, Bossenbroek L, Schilling J, Berger E (2022) Governance and Sustainability Challenges in the Water Policy of Morocco 1995–2020: Insights from the Middle Draa Valley. Water 14:2932. https://doi.org/10.3390/w14182932

Singh LK, Jha MK, Chowdary VM (2017) Multi-criteria analysis and GIS modeling for identifying prospective water harvesting and artificial recharge sites for sustainable water supply. J Clean Prod 142:1436–1456. https://doi.org/10.1016/j.jclepro.2016.11.163

Singhai A, Das S, Kadam AK et al (2019) GIS-based multi-criteria approach for identification of rainwater harvesting zones in upper Betwa sub-basin of Madhya Pradesh, India. Environ Dev Sustain 21:777–797. https://doi.org/10.1007/s10668-017-0060-4

Tahvili Z, Khosravi H, Malekian A et al (2021) Locating suitable sites for rainwater harvesting (RWH) in the central arid region of Iran. Sustain Water Resour Manag 7:10. https://doi.org/10.1007/s40899-021-00491-2

UN (2015) United Nations Sustainable Development. In: United Nations Sustainable Development.  https://www.un.org/sustainabledevelopment/ . Accessed 12 Jul 2022

USDA (1972) Soil Conservation Service (1972) National engineering handbook, Section “Data standardization.” Hydrology, Department of Agriculture, Washington, p 762

Yegizaw ES, Ejegu MA, Tolossa AT et al (2022) Geospatial and AHP Approach Rainwater Harvesting Site Identification in Drought-Prone Areas, South Gonder Zone, Northwest Ethiopia. J Indian Soc Remote Sens 50:1321–1331. https://doi.org/10.1007/s12524-022-01528-5

Yousif M, Bubenzer O (2015) Geoinformatics application for assessing the potential of rainwater harvesting in arid regions. Case study: El Daba’a area, Northwestern Coast of Egypt. Arab J Geosci 8:9169–9191. https://doi.org/10.1007/s12517-015-1837-0

Zaireg R (2017) Water shortages parch Moroccan towns, prompt protests | AP News.  https://apnews.com/article/africa-climate-business-climate-change-international-news-e5fb3935051c499aa8bd0d297bf878d0 . Accessed 10 Jul 2022

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All authors played significant roles in the study's conception and design. A.M conducted material preparation, data collection, and GIS analysis. Laboratory analyses were carried out by A.M, F.E.E, and A.K. A. A.E and A.M conducted field measurements. The initial draft of the manuscript was written by A.M and A.K, and all authors provided comments on earlier versions. The study was supervised by J.A.K, and M.B All authors read and approved the final manuscript.

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Moumane, A., Enajar, A.A., Ghazali, F.E.E. et al. GIS, remote sensing, and analytical hierarchy process (AHP) approach for rainwater harvesting site selection in arid regions: Feija Plain case study, Zagora (Morocco). Appl Geomat (2024). https://doi.org/10.1007/s12518-024-00585-4

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EY employee death: Nirmala Sitharaman calls for ‘stress management lessons’; Congress lashes out

According to the finance minister, the strength to handle pressure can be achieved through divinity.

Finance minister Nirmala Sitharaman on Sunday, in an apparent reference to the recent death of a young CA professional employed with Ernest and Young (EY), called for “stress management lessons” in colleges and universities. The Congress later slammed her for her remark.

Addressing an event at a college in Chennai, Sitharaman said that even if educational institutions impart good education and ensure jobs to students through campus recruitment, they should teach certain things, along with education, that are taught in the family.

“I was discussing an issue that has been in newspapers for the past two days. Our children go to colleges and universities for education and come out with flying colours. A company, without mentioning its name, its a partnership. There, a woman who had studied CA well, unable to cope with the work pressure, three days ago, we received news--she died, unable to cope with the pressure,” she said.

Also read: EY employee death: Rahul Gandhi speaks with Anna's parents, says ‘I promised…’

“Believe in God, we need to have God's grace. Seek God, and learn good discipline. Your Atma shakti will grow only from this. The inner strength will come only with growing Atma shakti…Educational institutions should bring in divinity and spirituality. Then only will our children get the inner strength. It will help in their progress and the country. That is my strong belief,” she added.

Congress lashes out

Sitharaman's remarks received a massive backlash from the Congress party.

“The ruling regime and the finance minister can only see the pain of corporate giants like Adani and Ambani, not the pain of the hardworking and toiling young generation where freshers like Anna are exploited by the greedy corporate system if they even succeed in getting a job in this era of historic joblessness,” Congress general secretary (Organisation), K C Venugopal, wrote in a post on X.

According to the Congress leader, it is “downright cruel” on the finance minister's part to blame Anna and her family and for suggesting that she should have learnt stress management at home.

Also read: 'Human rights…at workplace': Shashi Tharoor reacts to EY Pune employee Anna Sebastian Perayil's death

“This kind of victim blaming is despicable, and no words can convey the anger and disgust one feels because of such statements. How heartless can this government be? Have they lost all sense of empathy?” he questioned.

“The parents are still recovering from this terrible tragedy. The toxic work environment should have triggered an honest review of corporate practices and led to necessary reforms that protect employees,” Venugopal added.

EY employee's death

Anna Sebastian Perayil, a chartered accountant (CA) from Kerala who worked at EY Pune, died of cardiac arrest on June 20 following excessive workload and poor work culture at the company - as alleged by her mother. In an open letter, Anna's mother detailed her daughter's struggles with anxiety, sleeplessness, and stress due to an overwhelming workload, citing instances where her manager prioritised work over employee well-being.

According to her mother, Anna, who worked at EY's Pune for four months as part of the Audit team at S R Batliboi, a member firm of EY Global, also worked late into the night and on weekends. She returned to her PG accommodation completely exhausted on most days, her mother claimed.

Following an uproar over the incident, the Centre stepped in to say that it will investigate the work environment at Big Four accounting firm EY.

“Be it a white collar job or any worker, whenever a citizen of the country dies, it is natural to be saddened by it. The matter is being investigated, and action will be taken based on whatever facts are revealed in the investigation,” union minister of labour and employment, Mansukh Mandaviya, said.

(With inputs from ANI)

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