Crop planning based on rainfall variability for Bastar region of Chhattisgarh, India

Abstract


 Analysis of long-term rainfall data (1986-2018) of Bastar region revealed decreasing trend in total quantum of annual rainfall with varying frequency and distribution. The quantity of winter and summer rains decreased drastically during 2008-18 as compared to earlier two decades (1986-96 and 1997-2007). SW monsoon rain of 2008-18 was more than past two decades, whereas NE monsoon rain changed much in quantity except during 1997-2007. During 1986-96, the pre-monsoon shower was received in April, but later two decades the shower was received in May, which supports for summer ploughing and dry aerobic seeding. The cropping period almost synchronized between 22-43 standard meteorological week (SMW) reaching 93.11 mm per week as maximum rainfall. As the probability of 20 mm rainfall decreased from 75 to 50%, the crop yield got reduced by 30%. The mid-land rice with a probability of 13.47 to 16.07 mm rain per week supported growth phase during 17-21SMW. Whereas, upland rice maturing in 90-100 days could avoid dry spells, if the rice is managed by conservation furrows at the time of sowing. The summer ploughing is preferred with more than 40 mm rain in single day during March to April for mitigating dry spells. On the other hand, preparatory tillage and sowing were performed together in support of ripening niger and horsegram under probability of 75, 50 and 25% rain through crop planning. Maize and small millets reduced yield significantly when rainfall reached 75% deficit, whereas 25% deficit rain did not affect the yields.

Statistical analysis of recent rainfall variability and trend using a merged gauge and satellite time series data for the cotton zone of Mali

“…Consequently, in most African countries whose farming activity is mostly rainfed, robust and accurate estimation of the spatial and temporal distribution of rainfall including its trend are vital input parameters in order to secure sustainable agricultural activities (Ayalew et al 2012). The change in rainfall regime can't be assessed easily due to meteorological processes but long-term rainfall could scale up the planning of agriculture in the rainfed region (Pradhan et al 2020).…”

Assessing the long-term fluctuations in dry-wet spells over Indian region using Markov model in GEE cloud platform

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The climate change induces alteration in rainfall pattern thus increasing the scope for extreme events such as droughts, floods, unseasonal and extreme rainfall that has an adverse impact on agriculture threatening food security. Therefore, knowledge on the spatiotemporal distribution of rainfall pattern based on its longterm variability and frequency distribution are prime necessity for minimizing agricultural risk for sustainable agricultural production (Pradhan et al, 2020). Various agricultural operations viz.

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