Abstract
The temporal variability of rainfall in rainfed regions is one of the main factors for their low agricultural productivity. The future climate projections show an increased variability of rainfall, thus further impacting the rainfed agriculture. The change in rainfall pattern is expected to alter the cropping period and making the crop sowing date critical to mitigate crop failure. However, with enhanced temporal variability of rainfall, arriving at an optimal crop sowing date is a challenging task. One of the widely adopted measure to improve the agricultural productivity in the rainfed regions is water harvesting structures (WHS). This study evaluates the ability of the WHS in absorbing the shock of the temporal variability of the rainfall on the agricultural productivity. In addition, the efficacy of the structures in improving the agricultural productivity in the future climate projections is also evaluated. The proposed analysis is performed over Kondepi watershed in Andhra Pradesh, India, where water conservation measures are implemented by Government and Non-Government Organizations. The results of the study show that the WHS can minimize the sensitivity of the agricultural productivity to the crop sowing date. The extended availability of water in WHS resulted in removing the relationship between crop sowing date and crop productivity, thus exhibiting the ability of WHS in dams in absorbing the shock caused by the temporal variability of the rainfall. Further, the agricultural productivity was found to be increasing due to the presence of WHS in both current and future climate conditions.
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References
Al-Bakri J, Suleiman A, Abdulla F, Ayad J (2011) Potential impact of climate change on rainfed agriculture of a semi-arid basin in Jordan. Phys Chem Earth 36:125–134. https://doi.org/10.1016/j.pce.2010.06.001
Araya A, Habtu S, Hadgu KM, Kebede A, Dejene T (2010) Test of AquaCrop model in simulating biomass and yield of water deficient and irrigated barley (Hordeum vulgare). Agric. Water Manag 97:1838–1846. https://doi.org/10.1016/j.agwat.2010.06.021
Bird DN, Benabdallah S, Gouda N, Hummel F, Koeberl J, La Jeunesse I, Meyer S, Prettenthaler F, Soddu A, Woess-Gallasch S (2016) Modelling climate change impacts on and adaptation strategies for agriculture in Sardinia and Tunisia using AquaCrop and value-at-risk. Sci Total Environ 543:1019–1027. https://doi.org/10.1016/j.scitotenv.2015.07.035
Byakatonda J, Parida BP, Kenabatho PK, Moalafhi DB (2018) Influence of climate variability and length of rainy season on crop yields in semiarid Botswana. Agric For Meteorol 248:130–144. https://doi.org/10.1016/j.agrformet.2017.09.016
Challinor AJ, Wheeler TR (2008) Crop yield reduction in the tropics under climate change: Processes and uncertainties. Agric For Meteorol 148:343–356. https://doi.org/10.1016/j.agrformet.2007.09.015
Dile YT, Karlberg L, Daggupati P, Srinivasan R, Wiberg D, Rockström J (2016) Assessing the implications of water harvesting intensification on upstream-downstream ecosystem services: A case study in the Lake Tana basin. Sci Total Environ 542:22–35. https://doi.org/10.1016/j.scitotenv.2015.10.065
Farahani HJ, Izzi G, Oweis TY (2009) Parameterization and Evaluation of the AquaCrop Model for Full and Deficit Irrigated Cotton. Agron J 101:469–476. https://doi.org/10.2134/agronj2008.0182s
García-Vila M, Fereres E, Mateos L, Orgaz F, Steduto P (2009) Deficit Irrigation Optimization of Cotton with AquaCrop. Agron J 101:477–487. https://doi.org/10.2134/agronj2008.0179s
Garg KK, Karlberg L, Barron J, Wani SP, Rockstrom J (2012) Assessing impacts of agricultural water interventions in the Kothapally watershed, Southern India. Hydrol Process 26:387–404. https://doi.org/10.1002/hyp.8138
Hansen J, Ruedy R, Sato M, Lo K (2010) Global surface temperature change. Rev Geophys 48:RG4004. https://doi.org/10.1029/2010RG000345.1.INTRODUCTION
Harris K, Hawkins RA (1942) Irrigation Requirements of Cotton on Clay Loam Soils in the Salt River Valley. Arizona Agric. Exp. Stn. Bull. 181 Issue March.
Hoogenboom G (2000) Contribution of agrometeorolgy to simulation of crop production and its applications. Agric For Meteorol 103:137–157
Kahinda JM, Rockström J, Taigbenu AE, Dimes J (2007) Rainwater harvesting to enhance water productivity of rainfed agriculture in the semi-arid Zimbabwe. Phys Chem Earth Parts A/B/C 32:1068–1073. https://doi.org/10.1016/j.pce.2007.07.011
Kang Y, Khan S, Ma X (2009) Climate change impacts on crop yield, crop water productivity and food security - A review. Prog Nat Sci 19:1665–1674. https://doi.org/10.1016/j.pnsc.2009.08.001
Kant S, Seneweera S, Rodin J, Materne M, Burch D, Rothstein SJ, Spangenberg G (2012) Improving yield potential in crops under elevated CO2: Integrating the photosynthetic and nitrogen utilization efficiencies. Front Plant Sci 3:1–9. https://doi.org/10.3389/fpls.2012.00162
Karlberg L, Garg KK, Barron J, Wani SP (2015) Impacts of agricultural water interventions on farm income: An example from the Kothapally watershed, India. Agric Syst 136:30–38. https://doi.org/10.1016/j.agsy.2015.02.002
Keller DE, Fischer AM, Liniger MA, Appenzeller C, Knutti R (2017) Testing a weather generator for downscaling climate change projections over Switzerland. Int J Climatol 37:928–942. https://doi.org/10.1002/joc.4750
Kilsby CG, Jones PD, Burton A, Ford AC, Fowler HJ, Harpham C, James P, Smith A, Wilby RL (2007) A daily weather generator for use in climate change studies. Environ Model Softw 22:1705–1719. https://doi.org/10.1016/j.envsoft.2007.02.005
Kimball BA, Idso SB (1983) Increasing atmospheric CO2: effects on crop yield, water use and climate. Agric Water Manag 7:55–72. https://doi.org/10.1016/0378-3774(83)90075-6
Li P, Muenich RL, Chaubey I, Wei X (2019) Evaluating Agricultural BMP Effectiveness in Improving Freshwater Provisioning Under Changing Climate. Water Resour Manag 33:453–473. https://doi.org/10.1007/s11269-018-2098-y
Liu Y, Chen Q, Ge Q, Dai J, Qin Y, Dai L, Zou X, Chen J (2017) Modelling the impacts of climate change and crop management on phenological trends of spring and winter wheat in China. Agric For Meteorol 248:518–526. https://doi.org/10.1016/j.agrformet.2017.09.008
Loka DA, Oosterhuis DM (2012) Water Stress and Reproductive Development in Cotton. In: Oosterhuis, DM and Cothren, JT, Eds., Flowering and Fruiting in Cotton, Publ. Cotton Foundation, Memphis, 51–57
Magadza CHD (2000) Climate change impacts and human settlements in Africa: prospects for adaptation. Envioronmental Monit Assess 61: 193–205. https://doi.org/10.1023/A:1006355210516
Mandal S, Vema VK, Kurian C, Sudheer KP (2020) Improving the crop productivity in rainfed areas with water harvesting structures and deficit irrigation strategies. J Hydrol 586:124818. https://doi.org/10.1016/j.jhydrol.2020.124818
Mohammed A, Tana T, Singh P, Molla A, Seid A (2017) Identifying best crop management practices for chickpea (Cicer arietinum L.) in Northeastern Ethiopia under climate change condition. Agric Water Manag 194:68–77. https://doi.org/10.1016/j.agwat.2017.08.022
Pai DS, Sridhar L, Badwaik MR, Rajeevan M (2015) Analysis of the daily rainfall events over India using a new long period (1901–2010) high resolution (0.25° × 0.25°) gridded rainfall data set. Clim Dyn 45:755–776. https://doi.org/10.1007/s00382-014-2307-1
Raes D, Steduto P, Hsiao TC, Fereres E (2009) AquaCropThe FAO Crop Model to Simulate Yield Response to Water: II. Main Algorithms and Software Description. Agron J 101:438–447. https://doi.org/10.2134/agronj2008.0140s
Ramteke G, Singh R, Chatterjee C (2020) Assessing Impacts of Conservation Measures on Watershed Hydrology Using MIKE SHE Model in the Face of Climate Change. Water Resour Manag 34:4233–4252. https://doi.org/10.1007/s11269-020-02669-3
Reddy RV, Saharawat YS, George B (2017) Watershed management in South Asia: A synoptic review. J Hydrol 551:4–13. https://doi.org/10.1016/j.jhydrol.2017.05.043
Rockström J, Falkenmark M, Karlberg L, Hoff H, Rost S, Gerten D (2009) Future water availability for global food production: The potential of green water for increasing resilience to global change. Water Resour Res 45:1–16. https://doi.org/10.1029/2007WR006767
Soddu A, Deidda R, Marrocu M, Meloni R, Paniconi C, Ludwig R, Sodde M, Mascaro G, Perra E (2013) Climate Variability and Durum Wheat Adaptation Using the AquaCrop Model in Southern Sardinia. Procedia Environ Sci 19:830–835. https://doi.org/10.1016/j.proenv.2013.06.092
Steduto P, Hsiao TC, Raes D, Fereres E (2009) AquaCrop—The FAO Crop Model to Simulate Yield Response to Water: I. Concepts and Underlying Principles. Agron J 101:426–437. https://doi.org/10.2134/agronj2008.0139s
Valverde P, Serralheiro R, de Carvalho M, Maia R, Oliveira B, Ramos V (2015) Climate change impacts on irrigated agriculture in the Guadiana river basin (Portugal). Agric Water Manag 152:17–30. https://doi.org/10.1016/j.agwat.2014.12.012
Vema V, Sudheer KP, Chaubey I (2018) Hydrologic design of water harvesting structures through simulation-optimization framework. J Hydrol 563:460–469. https://doi.org/10.1016/j.jhydrol.2018.06.020
Vema V, Sudheer KP, Chaubey I (2017) Development of a hydrological model for simulation of runoff from catchments unbounded by ridge lines. J Hydrol 551:423–439. https://doi.org/10.1016/j.jhydrol.2017.06.012
Wani SP, Garg KK (2009) Watershed Management Concept and Principles [WWW Document]. ICRISAT. URL http://oar.icrisat.org/3914/1/1._Watershed_Management_Concept.pdf (accessed 5.10.14)
Wilks DS, Wilby RL (1999) The weather generation game: a review of stochastic weather models. Prog Phys Geogr 23:329–357. https://doi.org/10.1191/030913399666525256
Yang X, Chen F, Lin X, Liu Z, Zhang H, Zhao J, Li K, Ye Q, Li Y, Lv S, Yang P, Wu W, Li Z, Lal R, Tang H (2015) Potential benefits of climate change for crop productivity in China. Agric For Meteorol 208:76–84. https://doi.org/10.1016/j.agrformet.2015.04.024
Zhang Y, Engel B, Ahiablame L, Liu J (2015) Impacts of Climate Change on Mean Annual Water Balance for Watersheds in Michigan. USA Water 7:3565–3578. https://doi.org/10.3390/w7073565
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Vamsi Krishna Vema, K.P. Sudheer and Indrajeet Chaubey conceived the experiment. Data preparation, model simulations, analysis of results was performed by Vamsi Krishna Vema. Bias correction and downscaling of the climate data was done by Rohith AN. KP Sudheer and Indrajeet Chaubey supervised the research. Vamsi Krishna Vema wrote the original draft of manuscript and all authors contributed to the manuscript revisions.
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Vema, V., Sudheer, K.P., Rohith, A.N. et al. Impact of water conservation structures on the agricultural productivity in the context of climate change. Water Resour Manage 36, 1627–1644 (2022). https://doi.org/10.1007/s11269-022-03094-4
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DOI: https://doi.org/10.1007/s11269-022-03094-4