Author: rpuri

Econometric and statistical methods lead to informed decision-making and safeguards agricultural productivity in the face of climatic hazards in South Asia

By Prem Chand, ICAR-NIAP, India and Kaushik Bora, BISA-CIMMYT, India

Globally, climate extremes are adversely affecting agricultural productivity and farmer welfare. Farmers’ lack of knowledge about adaptation options may further exacerbate the situation. In the context of South Asia, which is home to rural farm-based economies with smallholder populations, tailored adaptation options are crucial to safeguard the region’s agriculture in response to current and future climate challenges. These resilience strategies encompass a range of risk reducing practices such as changing the planting date, conservation agriculture, irrigation, stress-tolerant varieties, crop diversification, and risk transfer mechanisms for example crop insurance. Practices such as enterprise diversification and community water conservation are also potential sector-specific interventions.

The Atlas of Climate Adaptation in South Asian Agriculture (ACASA) aims to identify hazard-linked adaptation options and prioritise them at a granular geographical scale. While doing so, it is paramount to consider the suitability of adaptation options from a socio-economic lens which varies across spatial and temporal dimensions. Further, calculation of scalability parameters such as economic, environmental benefit, and gender inclusivity for prioritized adaptation are important to aid climatic risk management and developmental planning in the subcontinent. Given the credibility of econometric and statistical methods, the key tenets of the approach that are being applied in ACASA are worth highlighting.

Evaluating the profitability of adaptation options

Profitability is among the foremost indicators for the feasible adoption of any technology. The popular metric of profitability evaluation is benefit-to-cost ratio. This is a simple measure based on additional costs and benefits because of adopting new technology. A benefit-to-cost ratio of more than one is considered essential for financial viability. Large-scale surveys such as cost of cultivation and other household surveys can provide cost estimates for limited adaptation options. Given the geographical and commodity spread, ACASA must resort to the meta-analysis of published literature or field trials for adaptation options. For example, a recent paper by International Food Policy Research Institute (IFPRI) based on meta-analysis shows that not all interventions result in a win-win situation with improvements in both tradable and non-tradable outcomes. While no-till wheat, legumes, and integrated nutrient management result in an advantageous outcome, there are trade-offs between the tradable and non-tradable ecosystem services in the cases of directed seed rice, organic manure, and agroforestry[1].

Quantification of adaptation options to mitigate hazards

Past studies demonstrate the usefulness of econometric methods when analyzing the effectiveness of adaptation options such as irrigation, shift in planting time, and crop diversification against drought and heat stress in South Asia. Compared to a simple cost-benefit approach, the adaptation benefits of a particular technology under climatic stress conditions can be ascertained by comparing it with normal weather conditions. The popular methods in climate economics literature are panel data regression and treatment-based models. Subject to data availability, modern methods of causal estimation and machine learning can be used to ascertain the robust benefits of adaptation options. Such studies, though available in literature, have compared limited adaptation options. A study by the Indian Council of Agricultural Research-National Institute of Agricultural Economics and Policy Research (ICAR-NIAP), based on ‘Situation Assessment Survey of Agricultural Households’ of National Sample Survey Office (NSSO), concluded that though crop insurance and irrigation effectively improve farm income and reduce farmers’ exposure to downside risk, irrigation is more effective than crop insurance[2].

Statistical models for spatial interpolation of econometric estimates

Since ACASA focuses on gridded analysis, an active area of statistical application is the spatial interpolation or downscaling of results to a more granular scale. Many indicators used for risk characterization are available at coarser geographical units or points from surveys. Kriging is a spatial interpolation method where there is no observed data. Apart from spatial interpolation of observed indicators, advanced Kriging methods can be potentially used to interpolate or predict the estimates of the econometric model.

 ACASA’s approach involves prioritizing adaptation options based on suitability, scalability, and gender inclusivity. Econometric and statistical methods play a crucial role in evaluating the profitability and effectiveness of various adaptation strategies from real world datasets. Despite challenges such as limited observational data and integration of econometric and statistical methods, ACASA can facilitate informed decision-making in climate risk management and safeguard agricultural productivity in the face of climatic hazards.

[1] Kiran Kumara T M, Birthal PS, Chand D and Kumar A. 2024. Economic Valuation of Ecosystem Services of Selected Interventions in Agriculture in India. IFPRI Discussion Paper 02250, IFPRI-South Asia Regional Office, New Delhi.

[2] Birthal PS, Hazrana J, Negi DS and Mishra A. 2022. Assessing benefits of crop insurance vis-a-vis irrigation in Indian agriculture. Food Policy 112:102348. https://doi.org/10.1016/j.foodpol.2022.102348

Gridded crop modelling builds an understanding of how climate change impacts crops, helping researchers to adapt agricultural methods and combat food insecurity.

Global temperatures are projected to warm by between 1.5-2 degrees Celsius by the year 2050, and 2-4 degrees Celsius by 2100. This is likely to change precipitation patterns, which will impact crop yields, water availability, food security, and agricultural resilience.

To prepare for these challenges, the Atlas of Climate Adaptation in South Asian Agriculture (ACASA) project use process-based simulation models that can predict crop growth, development, and yield in order to understand the response of crops to climate change. Models such as Decision Support System for Agrotechnology Transfer (DSSAT), InfoCrop, and Agricultural Production Systems Simulator (APSIM) facilitate the field scale study of the biophysical and biochemical processes of crops under various environmental conditions, revealing how they are affected by changing weather patterns.

The ACASA team, along with experts from Columbia University and the University of Florida, met for a three-day workshop in January 2024 to boost the work on spatial crop modelling. The aim was to design modelling protocol through a hands-on demonstration on high-performance computers. . When scientifically executed, gridded spatial crop modelling – even though complex and data-intensive – can be a great way to frame adaptation and mitigation strategies for improving food security, which is one of the ACASA project’s goals.

Decisions on data

The group decided to use DSSAT, APSIM, and InfoCrop for simulating the impact of climatic risks on crops such as rice, wheat, maize, sorghum millet, pigeon pea, chickpea, groundnut, soybean, mustard, potato, cotton, and more. They chose harmonized protocols across all three models with standard inputs, such as conducting simulations at 0.05 degrees. The model input data about weather, soil, crop varietal coefficients and crop management are being collected and processed for model input formats at 5 kilometer (km) spatial resolution.

A Python version called DSSAT-Pythia is now available to accelerate spatial and gridded applications. The programming for implementing InfoCrop on the Pythia platform is in progress. InfoCrop has been proven in India for past yield estimations, climate change spatial impact, and adaptation assessments for 12 crops.

For other crucial modelling components, a work plan was created including developing regional crop masks; crop zones based on mega-commodity environments as defined by CGIAR; production systems; crop calendars; and irrigated areas by crop. Genetic coefficients will then be calculated from measured past values and recent benchmark data of varietal units.

With this information, several adaptation options will be simulated, including changes in planting dates, stress-tolerant varieties, irrigation, and nitrogen fertilizer (quantity, methods, and technology), residue/mulching, and conservation tillage. The team will evaluate impact and adaptation benefits on yields, water, and nitrogen use efficiency based on the reported percentage change from the baseline data.

As the project progresses, this work will make strides towards realizing food security for the planet and increasing the resilience of smallholder farming practices.

Authors: Anooja Thomas, University of Florida; Apurbo K Chaki, BARI, Bangladesh; Gerrit Hoogenboom, University of Florida; S Naresh Kumar, ICAR-IARI, India

Using systematic literature review, ACASA has identified key climate adaptation options and assessed their effectiveness.

By Aniket Deo, BISA-CIMMYT India; Niveta Jain, ICAR-IARI India; Roshan B Ojha, NARC Nepal; and Sayla Khandoker, BARI Bangladesh

To address the vulnerability of increased climate risks which impact agriculture, it is imperative to identify location-specific adaptation options. The Atlas of Climate Adaptation in South Asian Agriculture (ACASA) team is working on identifying commodity specific hazards at different geographical regions and the key adaptation options aligned with geography and hazards. This has been done for major cereal crops (rice, wheat, and maize), coarse grains (millets), oilseeds (coconut, mustard), legumes and vegetable crops (chickpea, potato), livestock and fisheries. In ACASA, Systematic Literature Review (SLR) serves as a fundamental tool to identify key climate adaptation options and assess their effectiveness, considering agroecological factors.

Literature reviews are a customary approach for researchers to grasp existing knowledge and findings. The SLR methodically establishes clear research objectives, employs structured search queries to identify relevant literature, applies defined exclusion criteria, and extracts data for scientific analysis. This structured approach facilitates mapping the literature, validating findings, identifying gaps, and refining methodologies thereby minimizing biases, and ensuring comprehensive coverage of evidence.

Commodity-specific research questions, aligned with the problem/population, intervention, comparison/consequences, outcome, and time PICO(T) framework, have been used to guide the search process. By utilizing keywords specific to these questions, the ACASA team sourced literature from reputable databases such as Web of Science, Scopus, Google Scholar, and local databases of South Asian countries (Bangladesh, India, Nepal, and Sri Lanka). Local databases and gray literature further bolstered the understanding of local conditions and broadened the coverage of studied literature.

The searched literature was then filtered using the well-established Preferred Reporting Items for Systematic Reviews and Meta Analysis (PRISMA) framework. PRISMA provides a minimum set of evidence-based literature to be used for further analysis. Let us look at maize as an example of a commodity under analysis in ACASA. For maize, a total of 1,282 papers were identified and based on four exclusion criteria pertaining to adaptation options, quantitative assessment, hazard, and risk only of which 72 papers were shortlisted. PRISMA framework supported in getting a manageable dataset for in-depth analysis while ensuring transparency in the overall filtering process.

After filtering through PRISMA, a bibliometric analysis was conducted which contained research trend analysis, regional distribution patterns, adaptation option categorizations, and co-occurrence analysis. Useful patterns in popularity of studied adaptation options, hazards, and their linkages were observed through this analysis. For instance, drought was the most studied hazard, while pest diseases and economics were major hazard impacts studied for the maize literature. In terms of adaptation options, stress tolerant varieties were the most popular adaptation option. Further, co-occurrence analysis provided linkages between adaptation options and hazards, and demonstrated that researchers have also studied bundled technologies.

SLR helped understand the effectiveness of certain adaptation options. Going ahead, this step will be fully realized through “meta-analysis” which will be pivotal in quantifying the evidence and prioritizing adaptation options for different agroecologies.

SLR has proven to be an effective research method to build a comprehensive database that can be used across different thematic areas of ACASA. Adaptation options enlisted through SLR can be further substantiated through expert elicitations via heurism, crop modelling, cost-benefit analysis, and other important pillars of ACASA to identify efficient and cost-effective options.

SLR also provided the ACASA team with the opportunity to identify certain literature gaps such as uneven geographical coverage and excessive emphasis on certain adaptation options versus the rest. Conceptualization of systematically reviewing climate adaptation options in the South Asian context by integrating bibliometric and meta-analysis adds novelty to the current efforts of ACASA.