Estimation of Quantitative Losses of Rice (Oryzae Sativa L.) During Harvesting, Threshing and Cleaning in the Upper East Region: A Case Study at Tono Irrigation Project

Abstract

Loss assessment helps to identify constraints affecting the production and therefore the productivity of food crops. Information on loss assessment will thus assist in possible interventions needed to improve productivity. Quantitative losses associated with the production of rice (var. Jasmine 85) in the Kassena Nankana West District of the Upper East Region, one of the major rice producing areas in Ghana has not been adequately documented. A semi structured questionnaire was used to collect data from 84 rice farmers who were selected through a combination of multi-stage, purposive, and simple random sampling techniques. A multiple regression analysis was conducted to estimate quantitative losses and the factors that influenced the losses of rough rice. Kendall’s Coefficient of Concordance (WC) was used to determine the degree of agreement in the challenges farmers face at farm level. A technology-verification experiment was conducted on 12 farmer fields to estimate the yields and quantitative losses that do occur during harvesting and threshing at two (2) different harvesting times: improved harvesting time (35 days after heading) and farmer time of harvest (42 days after heading). A methodology adopted by Anwar (2010) was used to determine farmers’ harvest moisture content of rough rice. Rice cultivation on the Tono Irrigation Project was found to be dominated by males; only 38% were females. Therefore, males have purposefully made rice farming as a livelihood. Averagely, 0.68 ha field was under rice cultivation. Still, the average rough rice was produced at 2.73 mt/ha. The output saved for household consumption (0.7 mt/ha) was not significantly different (P>0.05) from the output (0.65 mt/ha) lost to the soil. Besides, the amount of rough rice that could have been saved for domestic consumption was equally lost at a percentage loss of 24%. From the perspective of the farmers, the losses were attributed to inappropriate time of harvesting. From the regression analysis, acreage (β=0.952 mt, P<0.01) and gender (β=0.162 mt, P≤0.05) were found to be positively related with losses in rough rice production. However, farm education (β= -0.656 mt, P<0.01) and adequate labour (β= -0.018 mt, P<0.05) were found to reduce field losses. The coefficient of determination (R2) was 0.655; which implies that 66% of the variation in the quantity of rough rice lost during harvesting and handling was explained by the specific variables in the model. The F-statistic was found to be significant at 1%. This implies that all the explanatory variables had a significant joint impact on the level of rough rice lost during harvesting and handling. The Kendall (Wa ) was estimated at 0.839. This suggests that the degree of agreement among the rankings of the challenges faced at farm gate was approximately at 84%. Therefore, the most pressing challenge was inaccessibility of tarpaulin and continuous adoption of improvised tarpaulins. From this study, the yield from improved time of harvest (4.8 ± 0.96 mt/ha) was (P<0.05) higher than farmer time of harvest (3.7 ± 1.2 mt/ha). Therefore, the losses incurred during improved harvest (5.88 ± 3.47%) was lower than farmer time of harvest (10.35 ± 1.94%). The harvest moisture content (20.88 ± 2.56%) was not significantly different (P>0.05) from the optimum harvest moisture content (22%). Increasing loss reduction awareness through farmer field schools and farmers adopting 35 days after heading as a harvesting time could lessen high levels of field losses, poverty index and improve domestic consumption.