Determinants of Post-harvest Losses of Pineapple: A Farm-level Study in Moulvibazar District, Bangladesh

Abstract

Aims: Post-harvest losses are acknowledged as one of the major reasons for fresh grain production, especially fruit production in most developing countries. Thus, the present study is highlighted the perishable nature of the food and inefficient post-harvest management are one of the key reasons for fruit (pineapple) losses in the Moulvibazar district.
 Study Design: This article is about determinants of post-harvest losses study and is placed on empirical analysis. It was carried out to find out what factors influence pineapple post-harvest losses at the farm level.
 Place and Duration of Study: Sreemangal, Barlekha, Kulaura, Juri, and Rajnagar Upazilas were purposively chosen for this study because they represent the five most important pineapple producing locations in the Moulvibazar district. The study period was the harvesting season of pineapple from April to June 2019.
 Methodology: Structured questionnaires were used to collect relevant information during face-to-face interviews with 320 pineapple farmers who were chosen using simple random sampling. Then the data were examined using descriptive and inferential statistics such as chi-square and factor analysis.
 Results: The descriptive analysis shows that, based on the ranking of percentage, all pineapple farmers (100%) were engaged in the marketing of pineapple in order to reduce losses by exploring both direct and indirect means to sell their produce. This means that in order to avoid losses, finding a market for pineapples is important. There was a substantial link between the level of education (P=0.049), farm size (P=0.000), farm experience (P=0.021), yield (P=0.000), and post-harvest losses, as per chi-square analysis. Furthermore, according to the findings of the factor analysis, harvesting, grading, sorting, storage, packaging, transportation, and marketing are all aspects that affect post-harvest losses of pineapple at the farm level in the study area. Implementing proper storage and packaging facilities can reduce pineapple farmers' losses and make it easier for them to market their products year-round. Moreover, sorting and grading pineapple increases its price, enabling more effective marketing.
 Conclusion: It has been shown that the transportation system has a substantial impact on pineapple distribution in the study area. Thus, it is logical to say that the transportation infrastructure should be upgraded in order to reduce pineapple post-harvest losses.

Combining farm and household surveys with modelling approaches to improve post-harvest loss estimates and reduce data collection costs

“…The most commonly found estimation approach is a multiple linear regression, using a set of explanatory variables in order to explain the dependent variable of on-farm losses. This approach was used for instance by Kumar et al [12] and Basavaraja et al [13] on grains, roots and tubers in India; Begum [14] and Khatun et al [15] on rice, wheat and tomato in Bangladesh; Arun et al [16] and Paneru et al [17] on a variety of commodities in Nepal; Adisa et al [18] on yam; Babalola et al [19] on tomato in Nigeria; Tadesse et al [20] on potato in Ethiopia; and Ambler et al [21] on cereals in Malawi. Some authors opted to use a double and semilogarithmic multiple regression analysis, as Folayan [22] on maize in Ethiopia, Aidoo et al [23] on tomato in Ghana, and Huang et al [24] on grains in China.…”

“…The most frequently used dependent variable is loss quantity in kilograms, as applied by Aidoo et al [23] and Aidu et al [34] in Ghana on tomato, Tadesse et al [20] on potato in Ethiopia, or Folayan [22] on maize in Nigeria. Four studies considered on-farm loss in kilograms per hectare, as a way to relate the loss quantity to the size of the farm, as Kumar et al [12] and Basavaraja et al [13] in India and Begum [14] or Khatun et al [15] in Bangladesh. On the other hand, six studies used loss percentage as the dependent variable, relating loss quantities to the total quantity produced, as Mebratie et al [35] and Amentae et al [29] on Ethiopia or Paneru et al [17] and Arun et al [16] on Nepal.…”