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Modelling sowing window effects on grain yield of cowpea varieties: An application of DSSAT CROPGRO-cowpea model in Burkina Faso

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Research Paper 06/06/2024
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Modelling sowing window effects on grain yield of cowpea varieties: An application of DSSAT CROPGRO-cowpea model in Burkina Faso

Thiombiano Célestin, Sourabié Soumaïla, Lado Adulrahman, Coulibaly Soumabere, Adnan Aminu Adnan, Bassam Abdulrahman Lawan, Serme Idriss, Batieno T. Benoit Joseph, Nerbewende Sawadogo, Tignegre JB De la Salle, Yelemou Barthelemy, Abdu Ahmed Manga, MA Hussaini
Int. J. Biosci.24( 6), 48-59, June 2024.
Certificate: IJB 2024 [Generate Certificate]
Key: CowpeaDSSAT modelGrain yieldSowing window

Abstract

Crop simulation models are part of the modern tools used for improving agricultural production performance. An application exercise of the DSSAT CROPGRO-cowpea model was carried out at two locations in Burkina Faso with the aim of contributing in enhancing agricultural practices for better yield of cowpea, through a seasonal analysis of the grain yield over 33-year period. The objectives were to (i) determining the optimum sowing window of cowpea varieties through a seasonal analysis of the grain yield at Kamboinsin and at Kouare, (ii) analysing the performance of Plant Production Department, Institute of the Environment and Agricultural Research of the DSSAT model in yield simulation in various environment. Simulations of the grain yield were performed using ten (10) sowing windows during the 33-years of experiment. The results showed that sowing period and the location affect the grain yield. For all the varieties (Gorom local, KVx396-4-5-2D, Moussa local and Tiligre), the highest grain yield was registered when sowing was done in early May. Mid-May to early June sowings lead to acceptable yields. Irrespective to the variety, late sowing (late June to late July) result in the lowest yields ranging between 980-327 kg ha-1 at Kamboinsin and 554-443 kg ha-1 at Kouare. The best optimum recommendable sowing window in cowpea production is early May, followed by mid-May to mid-June. Producing cowpea at Kamboinsin results in higher average grain yields than at Kouare. The DSSAT model can be considered as an efficient tool for simulating cowpea grain yield in various environments at different planting periods.

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Abdullahi IT, Uche IC, Bashir AB, Alpha YK, Adnan AA, Aloysius B, Adam MA. 2020. Modeling Planting‐Date Effects on Intermediate‐Maturing Maize in Contrasting Environments in the Nigerian Savanna: An Application of DSSAT Model. Agronomy 10, 871. https://doi.org/10.3390/agronomy10060871

Adnan AA, Jibrin MJ, Kamara AY, Abdulrahman BL, Shuaibu AS, Garba II. 2017. CERES‐Maize model for determining the optimum planting dates of early maturing maize varieties in northern Nigeria. Frontiers in Plant Science 8, 1–18.

Alberta G. 2019. Soil Moisture and Temperature Consideration. Factors that Contribute to Crop Growth. https://www.alberta.ca/soil‐moisture‐and‐temperature‐consideration.aspx#toc‐5

Amaral TA, Andrade C, Alves MEB, Silva DF. 2011. Applying CSM‐CERES‐Maize to define a sowing window for irrigated maize crop-The riacho’s farm case study. Interdisplinary Journal of Appl. Sciences 6, 38-53.

Archontoulis SV, Miguez FE. 2014. Evaluating APSIM Maize, Soil Water, Soil Nitrogen, Manure, and Soil Temperature Modules in the Midwestern United States. Agronomy Journal 106(3). https://doi.org/10.2134/agronj2013.0421

Elgadi JA. 2020. Calibration and Validation of the DSSAT Model with Experimental Data for Three Varieties of Wheat on Different Planting Dates. Journal of Misurata University for Agricultural Research.

Graef F, Haigis J. 2001. Spatial and temporal rainfall variability in the Sahel and its effects on farmers’ management strategies. Journal of Arid Environments, 48, 221–231.

Holzworth DP, Huth NI, DeVoil PG, Zurcher EG, Herrmann NI, McLean G, Chenu K, Erik J. 2014. APSIM – Evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software. Elsevier Science Publishers B. V., 62(C), 327–350. https://doi.org/10.1016/j.envsoft.2014.07.009.

Hoogenboom G, Porter CH, Boote KJ, Shelia V, Wilkens PW, Singh U, White JW, Asseng S, Lizaso JI, Moreno LP, Pavan W, Ogoshi R, Hunt LA, Tsuji GY, Jones JW. 2019. Decision Support System for Agrotechnology Transfer (DSSAT).

ISBNAT. 1990. Field & Laboratory Methods for the Collection of the IBSNAT Minim um Data Set for the Decision Support System for Agrotech’nologyTransfer (DSSAT V.2. 1) (IBSNAT Technical Report, Vol. 2).

Jibrin MJ, Kamara AY, Ekeleme F. 2012. Simulating Planting Dates and Cultivar Effects on Dryland Maize Production Using CERES-Maize Model. African Journal of Agriculture Research  7(40), 5530–5536.

Jones C, Czerniewic I. 2010. Describing or debunking? The net generation and digital natives. Journal of Computer Assisted Learning 26(5), 317–320.

Jones JW, Hoogenboom G, Porter CH, Boot KJ, Batchelor WD, Hunt LA, Wilken PW, Singh U, Gijsman AJ, Ritchie JT. 2003. The DSSAT cropping system model. European Journal of Agronomy 18(3–4), 235–265.

Keating BA, Carberry PS, Hammer GL, Probert ME, Robertson MJ, Holzworth D, Huth NI, Hargreaves JNG, Meinke H, Hochman Z, McLean G, Verburg K, Snow V, Dimes JP, Silburn M, Wang E, Brown S, Asseng S, Chapman S, Smith CJ. 2003. An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy. Elsevier 18(3–4), 267–288. https://doi.org/10.1016/S1161-0301(02) 00108-9.

Lin Y, Feng Z, Wu W, Yang Y, Zhou Y, Xu C. 2017. Potential impacts of climate change and adaptation on maize in northeast China. Agronomy Journal 109, 1476–1490.

Marteau R, Sultan B, Moron V, Alhassane A, Baron C, Traoré SB. 2011. The onset of the rainy season and farmer’s sowing strategy for pearl millet cultivation in South‐west Niger. Agric. For. Meteo. 151, 1356–1369.

Oke OF. 2016. Effects of agro‐climatic variables on yield of Zea mays L. in a humid tropical rainforest agroecosystem. Journal of Environment and Earth Science 6, 148-151.

Penning de Vries FWT, Jansen D, Ten Berge HFM, Bakema A. 1989. Simulation of ecophysiological processes of growth in several annual crops.

Rezzoug W, Gabreille B, Suleiman A, Benabdeli K. 2008. Application and evaluation of the DSSAT-wheat in the Tiaret region of Algeria. African Journal of Agricultural Research 4(3), 284–296.

Robertson MJ, Carberry PS, Huth NI, Turpin JE, Probert ME, Poulton PL, Bell M. 2002. Simulation of Growth and Development of Diverse Legume Species in APSIM. Australian Journal of Agricultural Research 53, 29–446.

Sallah PYK, Twumasi-Afriyie S, Kasei CN. 1997. Optimum planting dates for four maturity groups of maize varieties grown in the Guinea savanna zone. Ghana Journal of Agricltural Science 30, 63–70.

Thiombiano C, Lado A, Hussaini MA, Lawan BA, Serme I. 2022. Calibration and validation of CROPGRO-cowpea model of DSSAT for four cowpea varieties under drought stress. The International Journal of Science & Technoledge 10(12). https://doi.org/110.24940/theijst/2022/v10/i12/ST2212-007

Thiombiano C, Lado A, Coulibaly S, Bello TT, Batieno TBJ, Serme I, Gnankambary K, Sawadogo N, Ouedraogo MH, Tignegre JDS, Sawadogo M, Gaya MS, Abdulkadir A, Hussaini MA. 2023. Assessment of the Effects of Drought Stress at Seedling and Flowering Stages of Cowpea Development on Yield and Yield Attributes. Journal of Agriculture and Environmental Sciences 12(2), 68–80. https://doi.org/10.15640/jaes.v12n2a1

TNAU. 2016. Agrometeorology. Relative Humidity and Plant Growth. Tamil Nadu Agricultural University: Coimbatore, India.

Uehera G, Tsuji GY. 1993. The IBSNAT project. In Systems approach to agricultural development. Penning de Vries, F.W.T., Teng, P.S. (eds.) (pp. 505–514). Kluwer Academic Publishers, The Netherlands.

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