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Farming & Management

Influence of rainwater harvesting practices on the sorghum (Sorghum bicolor L.) grain yields in the sub-tropical deserts of Sudan ​


DOI: 10.31830/2456-8724.2022.FM-111    | Article Id: FM-111 | Page : 55-60
Citation :- Influence of rainwater harvesting practices on the sorghum (Sorghum bicolor L.) grain yields in the sub-tropical deserts of Sudan​. Farm. Manage. 7: 55-60
SHAMSEDDIN M AHMED, AMAR A ABDALLA, ADAM E AHMED AND AZHARIA A ELBUSHRA shams_id@yahoo.com
Address : Water Management and Irrigation Institute, University of Gezira, Sudan
Submitted Date : 15-11-2022
Accepted Date : 18-12-2022
Online Published:

Abstract

Food security is climate dependent in the Darfur region, Sudan; ultimately, the extreme drought events have derailed the region into “the world’s first climate change conflict”. Most of the ongoing climate resilience efforts in the region depend on rainwater harvesting (RWH) practices. The objective of this study is to be better understanding of the variability in the performance of the adopted RWH practices in the Darfur region. Datasets of 148 farmers were collected during the year 2022 through a structured questionnaire in the South Darfur state and analyzed based on two different modeling frameworks in the R package: the deep machine learning (the Bagging algorithms) and the logit models. The dependent variable is the rainfed sorghum grain yield, and the independent variables were RWH practices, education level, family size, and distance to the farm. The standardized precipitation index (SPI) explained that every two years the region is experiencing a drought event (1980-2015), with an annual rainfall of 396 mm ± 100 mm. About 82% of farmers mainly adopted RWH for food security in face of the decreased rainfall. The adopted RWH practices (mainly terracing) have increased sorghum grain yields by 72% - 147%.  The best grain yield is associated with spate irrigation, and the poorest with illiteracy. The large family size (> 9 people) and good education level offset the negative impacts of both illiteracy and the longer distance to the farm. The terracing and terracing + mulching + deep tillage (as a single package) practices were the most laborious and unsustainable practices in the Darfur region, and are not recommended, especially when the farm distance is > 5 km

Keywords

Bagging deep machine learning logit models rainwater harvesting sorghum yield  

References

Areidy, B. and Paolo, D. (2022). Future food security in Africa under climate change. Earth’s Fut. 10 : doi.org/10.1029/2022EF002651.
Bessah, E., Donkor, E., Raji, A. O., Taiwo, O. J., Ololade, O. O., Strapasson, A., Amponsah, S. K. and Agodzo, S. K. (2022). Factors affecting farmers' decision to harvest rainwater for maize production in Ghana. Front. Water. 4 : doi: org/10.3389/frwa.2022.966966.
Chtouki, M., Laaziz, F., Naciri, R., Garre, S., Nguyen, F. and Abdallah, O. (2022). Interactive effect of soil moisture content and phosphorus fertilizer form on chickpea growth, photosynthesis and nutrient uptake. Sci. Rep. 12 : doi: org/10.1038/s41598-022-10703-0.
Du, C., Lingling, L. and Zechariah, E. (2022). Effects of straw mulching and reduced tillage on crop production and environment: A review. Water 14 : doi: org/10.3390/w14162471.
FAO (2022). The State of Food Security and Nutrition in the World 2022. Repurposing food and agricultural policies to make healthy diets more affordable. Rome, FAO.
Friday, N. M., Ronald M., Justice N. and George N. (2022). Sorghum (Sorghum bicolor L.) yield response to rainwater harvesting practices in the semi-arid farming environments of Zimbabwe: A meta-analysis. Heliyon 8 : doi: org/10.1016/j.heliyon.2022.e09164.
Garg, K. K., Akuraju, V., Anantha, K. H., Singh, R., Whitebread, A. and Dixit, S. (2022). Identifying potential zones for rainwater harvesting interventions for sustainable intensification in the semi-arid tropics. Sci. Rep. 12 :  doi: org/10.1038/s41598-022-07847-4.
Ghahari, G., Hashemi, H. and Berndtsson, R. (2014). Spate irrigation of barely through floodwater harvesting in the Gareh-Bygone plain, Iran. Irrig. Drain. 63 : 599-611.
Hazarika, K., Goswami, R. K., Bharali, B., Kalita, P., Goswami, J. and Neog, B. (2022). Physiological performance of sweet sorghum in Assam. Res. Crop. 23 : 556-61.

Jeong, J. N., Resop, J. P., Mueller, N. D., Fleisher, D. H., Yun, K., Butler, E. E., Timlin, D. J., Shim, K. M., Gerber, J. S., Reddy, V. R. and Kim, S. H.  (2016). Random forests for global and regional crop yield predictions. PLoS ONE 11 :  doi: 10.1371/journal.pone.0156571.

Koutsoyiannis, D. (2006). Non stationarity versus scaling in hydrology. J. Hyd. 324 : 239-54.

Leng, G. and Hall, J. (2020). Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models. Environ. Res. Lett. 15 : 10.1088/1748-9326/ab7b24.

Maiti, R. K. and Singh, V. P. (2019). A review on mechanisms of resistance in sorghum to drought, high and low temperature and salinity†. Farm. Manage. 4 : 19-37.

 

Mehrabi, Z., Delzeit, R., Ignaciuk, A. et al. (2022). Research priorities for global food security under extreme events. One Earth 5 : 756-66.
Mekuria, K., Andualem K. and Ebrahim E. (2020). Adoption of rainwater harvesting and its impact on smallholder farmer livelihoods in Kutaber district, South Wollo Zone, Ethiopia. Cog. Food Agric. 6 : doi.org/10.1080/23311932.2020.1834910.
Mengistu, A. T. (2021). How small-scale farmers understand rain water harvesting technology? Evidence from Northern Ethiopia. Sci. World J. 2021 : doi: org/10.1155/2021/8617098.
Patroti, P., Madhusudhana, R., Rakshit, S., Sharma, K. K., Prabhakar, Samdur, M. Y., Nitesh, S. D., Elangovan, M. and Tonapi, V. A. (2022). Analysis of genetic diversity in exotic sorghum (Sorghum bicolor) germplasm and identification of trait specific superior accessions for post-rainy situation. Crop Res. 57 : 28-37.
Paudel, D., Boogaard, H., de Wit, A., Sander, J., Sjoukje, O., Christos, P. and Ioannis, N. (2021). Machine learning for large-scale crop yield forecasting. Agric. Syst. 187 : doi: org/10.1016/ j.agsy.2020.103016.
Shahhosseini, M., Hu, G., Huber, I. and Sotirios, V. (2021). Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt. Sci. Rep. 11 :  doi.org/10.1038/ s41598-020-80820-1.
Shamseddin, M. A. (2021). Modeling crop yields amidst climate change in the Nile basin (2040–2079). Model Earth Syst. Environ. 8 : 1977-90.
Sudan statistical book (2009). Statistical year book for the year 2009. Central Bureau of Statistics. The Ministry of the Cabinet. The Republic of Sudan.
Sultan, B., Defrance, D. and Iizumi, T. (2019). Evidence of crop production losses in West Africa due to historical global warming in two crop models. Sci. Rep. 9 : doi.org/10.1038/s41598-019-.49167-0.
Verhoeven, H. (2011). Climate change, conflict and development in Sudan: Global neo-malthusian narratives and local power struggles. Dev. Chang. 42 : 679-707.
Vogel, M., Donat E., Alexander, L. et al. (2019). The effects of climate extremes on global agricultural yields. Environ. Res. Lett. 14 : doi: 10.1088/1748-9326/ab154b.
Wang, J., Zhihua, P., Feifei, P., Di, H., Yuying, P., Guolin, H., Na, H., Ziyuan, Z., Wenjuan, Y., Jiale, Z., Ruiqi, P. and Zizhong, W. (2020). The regional water-conserving and yield-increasing characteristics and suitability of soil tillage practices in Northern China. Agric. Water Manag. 228 : doi: org/10.1016/j.agwat.2019.105883.
 

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