IDEAS home Printed from https://ideas.repec.org/a/eee/agisys/v151y2017icp174-183.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0308521X16305881
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agsy.2016.09.018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Aggarwal, P.K. & Banerjee, B. & Daryaei, M.G. & Bhatia, A. & Bala, A. & Rani, S. & Chander, S. & Pathak, H. & Kalra, N., 2006. "InfoCrop: A dynamic simulation model for the assessment of crop yields, losses due to pests, and environmental impact of agro-ecosystems in tropical environments. II. Performance of the model," Agricultural Systems, Elsevier, vol. 89(1), pages 47-67, July.
    2. Naresh Soora & P. Aggarwal & Rani Saxena & Swaroopa Rani & Surabhi Jain & Nitin Chauhan, 2013. "An assessment of regional vulnerability of rice to climate change in India," Climatic Change, Springer, vol. 118(3), pages 683-699, June.
    3. Naresh Kumar, S. & Aggarwal, P.K., 2013. "Climate change and coconut plantations in India: Impacts and potential adaptation gains," Agricultural Systems, Elsevier, vol. 117(C), pages 45-54.
    4. Webber, Heidi & Gaiser, Thomas & Ewert, Frank, 2014. "What role can crop models play in supporting climate change adaptation decisions to enhance food security in Sub-Saharan Africa?," Agricultural Systems, Elsevier, vol. 127(C), pages 161-177.
    5. Alary, V. & Corbeels, M. & Affholder, F. & Alvarez, S. & Soria, A. & Valadares Xavier, J.H. & da Silva, F.A.M. & Scopel, E., 2016. "Economic assessment of conservation agriculture options in mixed crop-livestock systems in Brazil using farm modelling," Agricultural Systems, Elsevier, vol. 144(C), pages 33-45.
    6. Aggarwal, P.K. & Kalra, N. & Chander, S. & Pathak, H., 2006. "InfoCrop: A dynamic simulation model for the assessment of crop yields, losses due to pests, and environmental impact of agro-ecosystems in tropical environments. I. Model description," Agricultural Systems, Elsevier, vol. 89(1), pages 1-25, July.
    7. Tendall, Danielle M. & Gaillard, Gérard, 2015. "Environmental consequences of adaptation to climate change in Swiss agriculture: An analysis at farm level," Agricultural Systems, Elsevier, vol. 132(C), pages 40-51.
    8. Leslie Lipper & Philip Thornton & Bruce M. Campbell & Tobias Baedeker & Ademola Braimoh & Martin Bwalya & Patrick Caron & Andrea Cattaneo & Dennis Garrity & Kevin Henry & Ryan Hottle & Louise Jackson , 2014. "Climate-smart agriculture for food security," Nature Climate Change, Nature, vol. 4(12), pages 1068-1072, December.
    9. Kattarkandi Byjesh & Soora Kumar & Pramod Aggarwal, 2010. "Simulating impacts, potential adaptation and vulnerability of maize to climate change in India," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 15(5), pages 413-431, June.
    10. Louhichi, Kamel & Kanellopoulos, Argyris & Janssen, Sander & Flichman, Guillermo & Blanco, Maria & Hengsdijk, Huib & Heckelei, Thomas & Berentsen, Paul & Lansink, Alfons Oude & Ittersum, Martin Van, 2010. "FSSIM, a bio-economic farm model for simulating the response of EU farming systems to agricultural and environmental policies," Agricultural Systems, Elsevier, vol. 103(8), pages 585-597, October.
    11. Jones, Peter G. & Thornton, Philip K., 2013. "Generating downscaled weather data from a suite of climate models for agricultural modelling applications," Agricultural Systems, Elsevier, vol. 114(C), pages 1-5.
    12. Belhouchette, Hatem & Louhichi, Kamel & Therond, Olivier & Mouratiadou, Ioanna & Wery, Jacques & Ittersum, Martin van & Flichman, Guillermo, 2011. "Assessing the impact of the Nitrate Directive on farming systems using a bio-economic modelling chain," Agricultural Systems, Elsevier, vol. 104(2), pages 135-145, February.
    13. Aggarwal, P.K. & Roetter, R.P.. & Kalra, N. & van Keulen, H. & Hoanh, C.T. & van Laar, H.H., 2001. "Land Use Analysis and PLanning for Sustainable Food Security: With an illustration for the State of Haryana, India," IRRI Books, International Rice Research Institute (IRRI), number 281817.
    14. Jones, Peter G. & Thornton, Philip K., 2015. "Representative soil profiles for the Harmonized World Soil Database at different spatial resolutions for agricultural modelling applications," Agricultural Systems, Elsevier, vol. 139(C), pages 93-99.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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).
    4. 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).
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paresh B. Shirsath & Vinay Kumar Sehgal & Pramod K. Aggarwal, 2020. "Downscaling Regional Crop Yields to Local Scale Using Remote Sensing," Agriculture, MDPI, vol. 10(3), pages 1-14, March.
    2. 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.
    3. Prabhu Pingali & Anaka Aiyar & Mathew Abraham & Andaleeb Rahman, 2019. "Transforming Food Systems for a Rising India," Palgrave Studies in Agricultural Economics and Food Policy, Palgrave Macmillan, number 978-3-030-14409-8, June.
    4. K. Viswanath & P. Sinha & S. Naresh Kumar & Taru Sharma & Shalini Saxena & Shweta Panjwani & H. Pathak & Shalu Mishra Shukla, 2017. "Simulation of leaf blast infection in tropical rice agro-ecology under climate change scenario," Climatic Change, Springer, vol. 142(1), pages 155-167, May.
    5. K. Hebbar & M. Venugopalan & A. Prakash & P. Aggarwal, 2013. "Simulating the impacts of climate change on cotton production in India," Climatic Change, Springer, vol. 118(3), pages 701-713, June.
    6. Pathak, H. & Wassmann, R., 2007. "Introducing greenhouse gas mitigation as a development objective in rice-based agriculture: I. Generation of technical coefficients," Agricultural Systems, Elsevier, vol. 94(3), pages 807-825, June.
    7. Naresh Kumar, S. & Aggarwal, P.K., 2013. "Climate change and coconut plantations in India: Impacts and potential adaptation gains," Agricultural Systems, Elsevier, vol. 117(C), pages 45-54.
    8. M. Sujithra & Subhash Chander, 2013. "Simulation of rice brown planthopper, Nilaparvata lugens (Stal.) population and crop-pest interactions to assess climate change impact," Climatic Change, Springer, vol. 121(2), pages 331-347, November.
    9. Kattarkandi Byjesh & Soora Kumar & Pramod Aggarwal, 2010. "Simulating impacts, potential adaptation and vulnerability of maize to climate change in India," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 15(5), pages 413-431, June.
    10. Fargue-Lelièvre, A. & Le Cœur, D. & Baudry, J., 2011. "Integrating farming techniques in an ecological matrix model: Implementation on the primrose (Primula vulgaris)," Ecological Modelling, Elsevier, vol. 222(4), pages 1002-1015.
    11. Britz, Wolfgang & Ciaian, Pavel & Gocht, Alexander & Kanellopoulos, Argyris & Kremmydas, Dimitrios & Müller, Marc & Petsakos, Athanasios & Reidsma, Pytrik, 2021. "A design for a generic and modular bio-economic farm model," Agricultural Systems, Elsevier, vol. 191(C).
    12. Annelie Holzkämper, 2017. "Adapting Agricultural Production Systems to Climate Change—What’s the Use of Models?," Agriculture, MDPI, vol. 7(10), pages 1-15, October.
    13. Trnka, M. & Muška, F. & Semerádová, D. & Dubrovský, M. & Kocmánková, E. & Žalud, Z., 2007. "European Corn Borer life stage model: Regional estimates of pest development and spatial distribution under present and future climate," Ecological Modelling, Elsevier, vol. 207(2), pages 61-84.
    14. Sulav Paudel & Lalit P. Sah & Mukti Devkota & Vijaya Poudyal & P.V. Vara Prasad & Manuel R. Reyes, 2020. "Conservation Agriculture and Integrated Pest Management Practices Improve Yield and Income while Reducing Labor, Pests, Diseases and Chemical Pesticide Use in Smallholder Vegetable Farms in Nepal," Sustainability, MDPI, vol. 12(16), pages 1-16, August.
    15. Singh, P. & Aggarwal, P. K. & Bhatia, V. S. & Murty, M. V. R. & Pala, M. & Oweis, T. & Benli, B. & Rao, K. P. C. & Wani, S. P., 2009. "Yield gap analysis: modelling of achievable yields at farm level," IWMI Books, Reports H041995, International Water Management Institute.
    16. Chopin, Pierre & Blazy, Jean-Marc & Guindé, Loïc & Wery, Jacques & Doré, Thierry, 2017. "A framework for designing multi-functional agricultural landscapes: Application to Guadeloupe Island," Agricultural Systems, Elsevier, vol. 157(C), pages 316-329.
    17. Selvaraj Krishnan & Subhash Chander, 2015. "Simulation of climatic change impact on crop-pest interactions: a case study of rice pink stem borer Sesamia inferens (Walker)," Climatic Change, Springer, vol. 131(2), pages 259-272, July.
    18. Bahareh Kamali & Karim C. Abbaspour & Bernhard Wehrli & Hong Yang, 2019. "A Quantitative Analysis of Socio-Economic Determinants Influencing Crop Drought Vulnerability in Sub-Saharan Africa," Sustainability, MDPI, vol. 11(21), pages 1-18, November.
    19. Parisa Paymard & Mohammad Bannayan & Reza Sadrabadi Haghighi, 2018. "Analysis of the climate change effect on wheat production systems and investigate the potential of management strategies," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 91(3), pages 1237-1255, April.
    20. Humblot, Pierre & Jayet, Pierre-Alain & Petsakos, Athanasios, 2017. "Farm-level bio-economic modeling of water and nitrogen use: Calibrating yield response functions with limited data," Agricultural Systems, Elsevier, vol. 151(C), pages 47-60.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agisys:v:151:y:2017:i:c:p:174-183. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agsy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.