期刊
ERWERBS-OBSTBAU
卷 65, 期 4, 页码 1139-1151出版社
SPRINGER
DOI: 10.1007/s10341-022-00738-y
关键词
Trend; Structural time series model; Principal component analysis; Zones; Classification
类别
This study analyzed the production trends of major fruit crops in Bihar and found a significant increasing trend in banana production. Zone I and IIIB in Bihar produced significantly more fruit than other zones. The study results can help researchers and policy makers identify low productivity areas and improve productivity to support sustainable income for farmers in flood-prone areas.
Flooding is a recurrent phenomenon in South Asian countries during the monsoon season. In the state of Bihar in eastern India, 55% of the geographical area is vulnerable to flood each year. Flood-tolerant fruit crops like banana (Musa sp.), mango (Mangifera indica), litchi (Litchi chinensis), and guava (Psidium guajava) provide an alternate source of income to farming communities in flood-prone areas where cereal crops would be damaged. Keeping in view the importance of fruit production as a mitigating option of flood risk, this study analyzed the trends in production of principal fruit crops of Bihar, i.e., banana, mango, litchi, and guava fruits. The 38 districts of Bihar, which were categorized into four zones (zone I, zone II, zone IIIA and zone IIIB), have been classified and mapped into high, medium, and low categories based on fruit productivity. For the study, annual time series data on fruit production and productivity in Bihar from 2001-2020 were used. The structural time series model (STSM), Kruskal-Wallis, and principal component analysis (PCA) were used for analysis . A significant increasing trend in banana production has been observed and is predicted through the year 2025. No significant increasing trend was observed for mango, litchi, or guava production. Zone I in Bihar produced significantly more banana, litchi, and mango than the other three zones. Zone IIIB produced significantly more guava. The districts were mapped into high, medium, and low productivity-based categories. Hence, the study results will be useful for researchers and policy makers to identify low productive areas and increase productivity through better management practices to support sustainable income to farmers in flood-prone areas.
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