https://doi.org/10.4081/ija.2022.2118
The impact of climate change on the productivity of cowpea (Vigna unguiculata) under three different socio-economic pathways
Submitted: 12 June 2022
Accepted: 21 November 2022
Published: 30 December 2022
Accepted: 21 November 2022
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Appendix: 72
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Crop models are useful tools for simulating the impact of climate change on crop growth, development and yield. This study assesses the impact of climate change on cowpea yield in soils with low levels of phosphorous content mainly in the Sudan Savanna and Forest Transition Zone of West Africa. A crop model solution within the general modelling framework SIMPLACE in combination with the output of four climate models for 3 contrasting shared socio-economic scenarios (SSP126, SSP370, and SSP585) was used to simulate the impact of climatic change on phenology, above ground biomass and yield parameters of cowpea. The simulations were carried out for Ouagadougou and Kumasi, representing the two major savanna biomes in West Africa (Sudan Savanna and Guinea Savanna). Previous field experimental data on the wide-spread cowpea genotype Asontem from a P-deficient soil at Kumasi (Ghana) were used to validate the SIMPLACE crop model solution. The model was able to simulate the impact of irrigation and fertilizer management on cowpea growth and yield assessment with adequate accuracy. Compared to historic simulations of the biomass and yield of cowpea, the model solution projected higher above ground biomass, and yield under the pre-dominant low input cropping systems for all the three SSPs as a result of the rise in CO2 and in spite of slightly shorted growing cycle length in both locations.
Highlights
- SIMPLACE crop model solution can reproduce cowpea yields for contrasting irrigation and fertilizer management.
- Compared to the baseline period, the model projected for the future period (2040 to 2070) slightly higher cowpea yields under three different socio-economic pathways.
- Cowpea biomass and yield remained high in future climate scenarios, despite slightly shorted growing cycle length, because of the rise in CO2.
- The projected trends have been similar in the Sudan and in the Guinea Savanna of West Africa.
Adam M, Belhouchette H, Corbeels M, Ewert F, Perrin A, Casellas E, Celette F, Wery J, 2012. Protocol to support model selection and evaluation in a modular crop modelling framework: An application for simulating crop response to nitrogen supply. Comput. Electron. Agric. 86:43-54.
DOI: https://doi.org/10.1016/j.compag.2011.09.009
Adusei G, Aidoo MK, Srivastava AK, Asibuo JY, Gaiser T, 2021. The variability of grain yield of some cowpea genotypes in response to phosphorus and water stress under field conditions. Agronomy 11:28.
DOI: https://doi.org/10.3390/agronomy11010028
Agbicodo EM, Fatokun CA, Muranaka S, Visser RGF, van Der CGL, 2009. Breeding drought tolerant cowpea: constraints, accomplishments, and future prospects. Euphytica 167:353-70.
DOI: https://doi.org/10.1007/s10681-009-9893-8
Alexandre G, Piebiep G, Ana B, Raúl DP, Henrique T, Eduardo ASR, Luis F, Miguel R, 2016. Cowpea (Vigna unguiculata L. Walp), a renewed multipurpose crop for a more sustainable agri-food system: Nutritional advantages and constraints. J. Sci. Food Agric. 96:2941-51.
DOI: https://doi.org/10.1002/jsfa.7644
Allen RG, Pereira LS, Raes D, Smith M, 1998. Crop evapotranspiration: guidelines for computing crop water requirements. Irrig. Drain. Paper 56. UN-FAO, Rome, Italy, p. 301.
Bastos EA, Folegatti MV, de Faria RT, de Andrade AS, Cardoso MJ, 2002. Simulation of growth and development of irrigated cowpea in Piauí State by CROPGRO model. Pesq. Agropec. Bras. Brasília 37:1381-7.
DOI: https://doi.org/10.1590/S0100-204X2002001000005
Chaves MM, Flexas J, Pinheiro C, 2009. Photosynthesis under drought and salt stress: regulation mechanisms from whole plant to cell. Annu. Bot. 103:551-60.
DOI: https://doi.org/10.1093/aob/mcn125
Choruma DJ, Balkovic J, Odume ON, 2019. Calibration and validation of the EPIC model for maize production in the Eastern Cape, South Africa. Agronomy 9:494.
DOI: https://doi.org/10.3390/agronomy9090494
Dogan E, Kirnak H, Copur O, 2007. Deficit irrigations during soybean reproductive stages and CROPGRO-soybean simulations under semi-arid climatic conditions. Field Crops Res. 103:154-9.
DOI: https://doi.org/10.1016/j.fcr.2007.05.009
Dovlo FE, Williams CE, Zoaka L, 1976. Cowpeas: home preparation and use in West Africa. Int. Dev. Res. Cent. 22:96.
Edwin KK, Aidan S, Tafadzwanashe M, 2021. Calibration and validation of the AquaCrop model for full and deficit irrigated cowpea (Vigna unguiculata (L.) Walp). Phys. Chem. Earth Parts A/B/C 124:1474-7065.
Egbadzor KF, Yeboah M, Offei SK, Ofori K, Danquah EY, 2013. Farmers’ key production constraints and traits desired in cowpea in Ghana. J. Agric. Ext. Rural Develop. 5:14-20.
Espadafor M, Couto L, Resende M, Henderson DW, Garcia-Vila M, Fereres E, 2017. Simulation of the responses of dry beans (Phaseolus vulgaris L.) to irrigation. Trans. ASABE 60:1983-94.
DOI: https://doi.org/10.13031/trans.12386
Ewansiha SU, Singh BB, 2006. Relative drought tolerance of important herbaceous legumes and cereals in the moist and semi-arid regions of West Africa. J. Food Agric. Environ. 4:188-90.
Eyshi-Rezaei E, Siebert S, Ewert F, 2015. Impact of data resolution on heat and drought stress simulated for winter wheat in Germany. Eur. J. Agron. 65:69-82.
DOI: https://doi.org/10.1016/j.eja.2015.02.003
Faye B, Webber H, Diop M, Mbaye ML, Owusu-Sekyeree JD, Naab JB, Gaiser T, 218. Potential impact of climate change on peanut yield in Senegal, West Africa. Field Crops Res. 219:148-59.
DOI: https://doi.org/10.1016/j.fcr.2018.01.034
Frimpong HN, Kerr RB, 2015. The relative importance of climate change in the context of multiple stressors in semi-arid Ghana. Global Environ. Change 32:40-56.
DOI: https://doi.org/10.1016/j.gloenvcha.2015.03.003
Gaiser T, Perkons U, Kupper PM, Kautz T, Puschmann DU, Ewert F, Enders A, Krauss G, 2013. Modeling biopore effects on root growth and biomass production on soils with pronounced sub-soil clay accumulation. Ecol. Model. 256:6-15.
DOI: https://doi.org/10.1016/j.ecolmodel.2013.02.016
Habte A, Worku W, Gayler S, Ayalew D, Mamo G, 2020. Model-based yield gap analysis and constraints of rainfed sorghum production in Southwest Ethiopia. J. Agric. Sci. 158:855-69.
DOI: https://doi.org/10.1017/S0021859621000435
Hartmann A, Šimůnek J, Aidoo MK, Seidel SJ, Lazarovitch N, 2018. Implementation and application of a root growth module in HYDRUS. Vadose Zone J. 17:1-16.
DOI: https://doi.org/10.2136/vzj2017.02.0040
IPCC, 2014. Climate change 2014: impacts, adaptation, and vulnerability. part B: regional aspects. In: Barros VR, Field CB, Dokken DJ, Mastrandrea MD, Mach KJ, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (Eds.), Contribution of Working Group II to The Fifth Assessment Report of The Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 688.
Kanda EK, Senzanie A, Mabhaudhi, 2021. Calibration and validation of the AquaCrop model for full and deficit irrigated cowpea (Vigna unguiculata (L.) Walp). Physics Chem. Earth Parts A/B/C 124:1474-7065.
DOI: https://doi.org/10.1016/j.pce.2020.102941
Legesse EE, Srivastava AK, Kuhn A, Gaiser T, 2019. Household welfare implications of better fertilizer access and lower use inefficiency: long-term scenarios for Ethiopia. Sustainability 11:3952.
DOI: https://doi.org/10.3390/su11143952
Liu Y, Chen Q, Ge Q, Dai J, 2018. Spatiotemporal differentiation of changes in wheat phenology in China under climate change from 1981 to 2010. Sci. China Earth Sci. 61:1088-97.
DOI: https://doi.org/10.1007/s11430-017-9149-0
Mearns LO, Rosenzweig C, Goldberg R, 1997. Mean and variance change in climate scenarios: methods, agricultural applications, and measures of uncertainty. Clim. Change 35:367-96.
DOI: https://doi.org/10.1023/A:1005358130291
Niang I, Ruppel OC, Abdrabo MA, Essel A, Lennard C, Padgham J, Urquhart P, 2014. Africa. In: Barros VR, Field CB, Dokken DJ, Mastrandrea MD, Mach KJ, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (Eds.), Climate Change 2014: Impacts, adaptation, and vulnerability. Part B: regional aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1199-1265.
Nelson GC, Rosegrant MW, Koo J, 2009. Climate change impacts on agriculture and costs of adaptation (Research report). International Food Policy Research Institute, Washington, DC, USA.
Ortiz BV, Hoogenboom G, Vellidis G, Boote K, Davis RF, Perry C, 2009. Adapting the CROPGRO-Cotton model to simulate cotton biomass and yield under southern root-knot nematode parasitism. Trans. Am. Soc. Agric. Biol. Engine. 52:2129-40.
DOI: https://doi.org/10.13031/2013.29201
Oteng-Darko P, Yeboah S, Addy SNT, Amponsah S, Owusu Danquah E, 2012. Crop modelling: A tool for agricultural research - a review. J. Agric. Res. Develop. 1:001-6.
Pejić B, Mačkić K, Milošev D, Peksen E, Šeremešić S, Jaćimović G, Ćirić V, 2016. Response of cowpea genotypes to water stress in temperate climatic conditions. Genetika 48:463-72.
DOI: https://doi.org/10.2298/GENSR1602463P
Raes D, Steduto P, Hsiao TC, Fereres E, 2009. AquaCrop - the FAO crop model to simulate yield response to water: II. Main algorithms and software description Agron. J. 101:438-47.
DOI: https://doi.org/10.2134/agronj2008.0140s
Ringler C, 2008. Climate variability and change impact on water and food outcomes. International Food Policy Research Institute (IFPRI), Washington, DC, USA.
Rosenzweig C, Jones JW, Hatfield JL, Ruane AC, Boote KJ, Thorbum P, Antle JM, Nelson, GC, Porter C, Janssen S, Asseng S, Basso B, Ewert F, Wallach D, Baigorria G, Winter JM, 2013. The Agricultural Model intercomparison and Improvement Project (AgMIP): protocols and pilot studies. Agric. For. Meteorol. 170:166-82.
DOI: https://doi.org/10.1016/j.agrformet.2012.09.011
Ruane AC, Cecil LD, Horton RM, Gordon R, McCollum R, Brown D, Killough B, Goldberg R, Greeley AP, Rosenzweig C, 2013. Climate change impact uncertainties for maize in Panama: farm information, climate projections, and yield sensitivities. Agric. For. Meteorol. 170:132-45.
DOI: https://doi.org/10.1016/j.agrformet.2011.10.015
Seneviratne SI, Nicholas N, Easterling D, Goodness CM, Kanae S, Kossin J, Luo Y, Marengo J, McInnes K, Rahimi M, Reichstein M, Sorteberg A, Vera C, Zhang X, 2012. Changes in climate extremes and their impacts on the natural physical environment. In: Barros CBV, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner GK, Allen SK, Tignor M, Midgley PM (Eds.), Managing the risks of extreme events and disasters to advance climate change adaptation. A Special Report on Working Groups I and II of the Intergovernmental Panel on Climate Change Field Cambridge University Press, Cambridge, UK/New York, NY, USA, pp. 109-230.
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. 101:426-37.
DOI: https://doi.org/10.2134/agronj2008.0139s
Setiyono TD, Cassman KG, Specht JE, Dobermann A, Weiss A, Yangd H, Conley SP, Robinson AP, Pedersen P, De Bruin JL, 2010. Simulation of soybean growth and yield in near-optimal growth conditions. Field Crops Res. 119:161-74.
DOI: https://doi.org/10.1016/j.fcr.2010.07.007
Singh B, Ajeigbe H, Tarawali S, Fernandez-Rivera S, Abubakar M, 2003. Improving the production and utilization of cowpea as food and fodder. Field Crop. Res. 84:169-77.
DOI: https://doi.org/10.1016/S0378-4290(03)00148-5
Srivastava AK, Mboh CM, Gaiser T, Webber H, Ewert F, 2016. Effect of sowing date distributions on simulation of maize yields at regional scale-A case study in Central Ghana, West Africa. Agric. Syst. 147:10-23.
DOI: https://doi.org/10.1016/j.agsy.2016.05.012
Srivastava AK, Mboh CM, Gaiser T, Webber H, Ewert F, 2018. Climate change impact under alternate realizations of climate scenarios on maize yield and biomass in Ghana. Agric. Syst. 159:157-74.
DOI: https://doi.org/10.1016/j.agsy.2017.03.011
Thorp KR, Barnes EM, Hunsaker DJ, Kimball BA, White JW, Nazareth VJ, Hoogenboom G, 2014. Evaluation of CSM-CROPGRO-Cotton for simulating effects of management and climate change on cotton growth and evapotranspiration in an arid environment. Trans. Am. Soc. Agric. Biol. Engine. 57:1627-42.
DOI: https://doi.org/10.13031/trans.57.10612
Tovihoudji Pierre G, Akponikpè P, Irénikatché B, Agbossou Euloge K, Bielders Charles L, 2019. Using the DSSAT model to support decision making regarding fertilizer microdosing for maize production in the sub-humid Region of Benin. Front. Environ. Sci. 7:13.
DOI: https://doi.org/10.3389/fenvs.2019.00013
van der Kooi CJ, Reich M, Löw M, De Kok LJ, Tausz M, 2016. Growth and yield stimulation under elevated CO2 and drought: a meta-analysis on crops. Environ. Exp. Botany 122:150-7.
DOI: https://doi.org/10.1016/j.envexpbot.2015.10.004
van Oijen M, Ewert F, 1999. The effects of climatic variation in Europe on the yield response of spring wheat cv. Minaret to elevated CO2 and O3: an analysis of open-top chamber experiments by means of two crop growth simulation models. Eur. J. Agron. 10:249-64.
DOI: https://doi.org/10.1016/S1161-0301(99)00014-3
Wang L, Zheng YF, Yu Q, Wang EL, 2007. [Applicability of agricultural production systems simulator (APSIM) in simulating the production and water use of wheat-maize continuous cropping system in North China Plain]. Ying Yong Sheng Tai Xue Bao 18:2480-6.
Williams JR, Izaurralde RC, 2006. The APEX model. In: V.P. Singh & D.K. Frevert (Eds.), Watershed models. CRCPress, Taylor & Francis, Boca Raton, FL, USA, pp. 437-482.
DOI: https://doi.org/10.1201/9781420037432.ch18
Wolf J, 2012. User Guide for LINTUL5: simple generic model for simulation of crop growth under potential, water limited and nitrogen, phosphorus and potassium limited conditions. Wageningen UR, pp. 1-63.
Yildiz H, Mermer A, Aydoğdu M, Şi̇mşek O, 2015. Forecasting of winter wheat yield for Turkey using water balance model. 2015 Fourth International Conference on Agro-Geoinformatics (Agro-geoinformatics), Istanbul, Turkey. Geoinformatics 7248135:352-356.
DOI: https://doi.org/10.1109/Agro-Geoinformatics.2015.7248135
Xiao-Xu L, Hui J, Jian-Dong W, Shi-Hong G, Guang-Yong, 2022. Impact of climate change on wheat yield and quality in the Yellow River Basin under RCP8.5 during 2020-2050. Adv. Climate Change Res. 2022:1674-9278.
How to Cite
Adusei, G., Aidoo, M. K., Srivastava, A. K. ., Asibuo, J. Y. ., & Gaiser, T. . (2022). The impact of climate change on the productivity of cowpea (Vigna unguiculata) under three different socio-economic pathways. Italian Journal of Agronomy, 17(4). https://doi.org/10.4081/ija.2022.2118
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