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Prioritizing climate-smart agricultural land use options at a regional scale

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  • Shirsath, Paresh B.
  • Aggarwal, P.K.
  • Thornton, P.K.
  • Dunnett, A.

Abstract

The promotion of climate-smart agriculture in different parts of the world requires a clear understanding of its relative suitability, costs and benefits, and the environmental implications of various technological interventions in a local context under current and future climates. Such data are generally difficult to obtain from the literature, field surveys and focused group discussions, or from biophysical experiments. This article describes a spreadsheet-based methodology that generates this information based on a region specific production function and ‘target yield’ approach in current and future climate scenarios. Target yields are identified for homogeneous agroecological spatial units using published crop yield datasets, crop models, expert judgement, biophysical land characterisations, assessment of yield gaps and future development strategies. Validated production/transfer functions are used to establish relationships between inputs (water, seed, fertilizer, machinery, energy, labour, costs) and outputs (crop yields, residues, water and fertiliser use efficiencies, greenhouse gas emissions, financial returns). The process is repeated for all spatial units of the region, identified through detailed mapping of critical biophysical factors, and for all suitable current and potential agronomic production technologies and practices. The application of this approach is illustrated for prioritizing agronomic interventions that can enhance productivity and incomes, help farmers adapt to current risk, and decrease greenhouse gas emissions in current and future climates for the flood- and drought-prone state of Bihar in north-eastern India. In general, climate smartness increases with advanced technologies. Yield is the least limiting while emission is the most limiting factor across the entire crop-technology portfolio for climate smartness. Finally, we present a robust climate smart land use plan at district level in Bihar under current and future climate scenarios.

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  • Shirsath, Paresh B. & Aggarwal, P.K. & Thornton, P.K. & Dunnett, A., 2017. "Prioritizing climate-smart agricultural land use options at a regional scale," Agricultural Systems, Elsevier, vol. 151(C), pages 174-183.
    • Handle: RePEc:eee:agisys:v:151:y:2017:i:c:p:174-183
    DOI: 10.1016/j.agsy.2016.09.018
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    1. Das, Usha & Ansari, M.A. & Ghosh, Souvik, 2022. "Effectiveness and upscaling potential of climate smart agriculture interventions: Farmers' participatory prioritization and livelihood indicators as its determinants," Agricultural Systems, Elsevier, vol. 203(C).
    2. Qaisar Saddique & Huanjie Cai & Jiatun Xu & Ali Ajaz & Jianqiang He & Qiang Yu & Yunfei Wang & Hui Chen & Muhammad Imran Khan & De Li Liu & Liang He, 2020. "Analyzing adaptation strategies for maize production under future climate change in Guanzhong Plain, China," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(8), pages 1523-1543, December.
    3. Durga, Neha & Sikka, Alok & Verma, Shilp & Mitra, Archisman & Amarasinghe, Upali & Mahapatra, Smaranika, 2022. "Agricultural Water Management (AWM) typologies: targeting land-water management interventions towards improved water productivity," IWMI Books, Reports H051383, International Water Management Institute.
    4. Adelhart Toorop, Roos & Ceccarelli, Viviana & Bijarniya, Deepak & Jat, Mangi Lal & Jat, Raj Kumar & Lopez-Ridaura, Santiago & Groot, Jeroen C.J., 2020. "Using a positive deviance approach to inform farming systems redesign: A case study from Bihar, India," Agricultural Systems, Elsevier, vol. 185(C).
    5. Dunnett, A. & Shirsath, P.B. & Aggarwal, P.K. & Thornton, P. & Joshi, P.K. & Pal, B.D. & Khatri-Chhetri, A. & Ghosh, J., 2018. "Multi-objective land use allocation modelling for prioritizing climate-smart agricultural interventions," Ecological Modelling, Elsevier, vol. 381(C), pages 23-35.
    6. Alam, Mohammad Faiz & Durga, Neha & Sikka, Alok & Verma, Shilp & Mitra, Archisman & Amarasinghe, Upali & Mahapatra, Smaranika, 2022. "Agricultural Water Management (AWM) typologies: targeting land-water management interventions towards improved water productivity," IWMI Reports 329168, International Water Management Institute.
    7. Arenas-Calle, Laura N. & Ramirez-Villegas, Julian & Whitfield, Stephen & Challinor, Andrew J., 2021. "Design of a Soil-based Climate-Smartness Index (SCSI) using the trend and variability of yields and soil organic carbon," Agricultural Systems, Elsevier, vol. 190(C).
    8. Hongpeng Guo & Yujie Xia & Chulin Pan & Qingyong Lei & Hong Pan, 2022. "Analysis in the Influencing Factors of Climate-Responsive Behaviors of Maize Growers: Evidence from China," IJERPH, MDPI, vol. 19(7), pages 1-17, April.
    9. Thornton, Philip K. & Whitbread, Anthony & Baedeker, Tobias & Cairns, Jill & Claessens, Lieven & Baethgen, Walter & Bunn, Christian & Friedmann, Michael & Giller, Ken E. & Herrero, Mario & Howden, Mar, 2018. "A framework for priority-setting in climate smart agriculture research," Agricultural Systems, Elsevier, vol. 167(C), pages 161-175.
    10. Nouri, Milad & Homaee, Mehdi & Bannayan, Mohammad & Hoogenboom, Gerrit, 2017. "Towards shifting planting date as an adaptation practice for rainfed wheat response to climate change," Agricultural Water Management, Elsevier, vol. 186(C), pages 108-119.
    11. Paresh B. Shirsath & Pramod K. Aggarwal, 2021. "Trade-Offs between Agricultural Production, GHG Emissions and Income in a Changing Climate, Technology, and Food Demand Scenario," Sustainability, MDPI, vol. 13(6), pages 1-13, March.
    12. Mohammad Valipour & Jens Krasilnikof & Stavros Yannopoulos & Rohitashw Kumar & Jun Deng & Paolo Roccaro & Larry Mays & Mark E. Grismer & Andreas N. Angelakis, 2020. "The Evolution of Agricultural Drainage from the Earliest Times to the Present," Sustainability, MDPI, vol. 12(1), pages 1-30, January.

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