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In China, a major grain producer, rice losses during harvest cannot be ignored. The purpose of this paper is to assess the extent of rice harvest losses in major grain-producing areas in China by a sampling survey, and to further analyze the main factors influencing rice harvest losses using an ordered multinomial logistic model.

In this study, data were collected using a multi-stage sampling method from ten major rice-producing provinces/regions in China. On this basis, five counties were selected from each of these provinces/regions. In the actual survey, house numbers were randomly selected, and then corresponding farmer households were visited by trained investigators.

The survey found that 56.22 percent of respondents believed that rice harvest losses were 4 percent or lower in China, though there were differences among the provinces. The proportion of family business income, planting scale, mechanization level, timely harvest, and operational meticulousness had negative effects on rice harvest losses. On the other hand, farmers’ experience of employment as migrant workers had a positive effect on rice harvest losses. In addition, bad weather and short handedness during harvest significantly increased rice harvest losses.

The meaning of rice harvest losses was defined based on previous research findings on the definition of grain harvest losses and the realities in China. The current rice harvest losses in different areas in China were analyzed based on sampling survey data from 957 farmers in ten provinces in China.

Post-harvest losses are becoming a huge issue worldwide and are predominantly severe in developing countries. Seeking ways to control post-harvest losses is important because losses decrease farm income by more than 15% for approximately 480 million small-scale farmers.

The study engaged Wave 4 (2018/2019) of the Living Standards Measurement Studies–Integrated Survey on Agriculture, to examine the impact of soil technology such as fertilisers, herbicides, pesticides and certified crops on post-harvest losses in Nigeria. The study engaged descriptive statistics, logit regression and propensity score matching (PSM) to analyse the data.

The study found that approximately 38% of the household harvest was lost along the value chain. In addition, the results showed that among the indicators of soil technology, crop certification has a significant impact on the reduction of post-harvest losses. The implication is that from the nearest neighbour and kernel-based matching, the use of certified crops by households contributed to 1.62 and 1.36% reduction in post-harvest losses, respectively. In contrast, pesticide, herbicide and fertiliser use had no significant impact on post-harvest losses.

One of the limitations is that this study applied the PSM, the model did not account for endogeneity. Therefore, in examining this concept, further studies should consider applying other impact model such as the difference-in-difference to account for endogeneity.

While previous studies have examined how ICT adoption, storage mechanisms and value chain among others help to minimise post-harvest losses, the aspect of how soil technology can reduce post-harvest losses has been a subject of exclusion in the extant literature. This study empirically examines the impact of soil technology adoption on post-harvest losses in Nigeria.

The purpose of this paper is to review the post-harvest loss experience of several Asia-Pacific economies to analyse the potential impacts of reduction of such losses using a range of remedial measures.

A conceptual framework has been developed and then applied to a case study based on several Asia-Pacific economies to provide an empirical basis for the analysis in the paper.

Limited access to vital farm inputs and credit, poor infrastructure and lack of technical and market information are some of the critical challenges confronting many small farmers in developing economies including those in the selected case-study countries. The estimated “food savings” are considerable if Asia-Pacific Economic Cooperation’s pledge to reduce food losses and waste by 10 per cent by 2020, relative to the 2011-2012 levels is realised in the case-study economies.

Further work is urgently required to collect more up-to-date data on food losses along the food supply chain, including post-harvest losses, in many economies across the world, including the Asia-Pacific region.

The analysis of post-harvest losses is underpinned by a conceptual framework that has been developed and applied to several Asia-Pacific economies.

The purpose of this paper is to analyse the impacts of storage losses and market development on the maize-selling behaviours of rural households in China.

Based on the survey data of 543 households from nine major maize production provinces in China, the authors introduce storage losses to a household's maize-selling decision-making model and use fractional logit model and ordered probit model to empirically analyse the impact of maize storage losses and market development on household maize-selling decisions in China. To overcome potential endogeneity problems, the authors select the weather at drying (whether bad weather occurs during the drying process) and harvest loss as instrumental variables and re-estimate the model.

The results show that increased storage losses prompt farmers to increase the proportion of maize sold within three months after harvest and sell maize in advance. Meanwhile, the degree of market development has a significant impact on farmers' maize-selling decisions. Other factors, such as the maize output, non-agricultural employment and awareness of loss control, also affect farmers' maize-selling behaviours.

The government should promote advanced storage facilities, reduce household storage losses, decrease the phenomenon of centralised sales after harvest and help farmers freely choose the suitable time for sales. The government also needs to strengthen market information releases and publicity, reduce transaction costs and help farmers make reasonable sales decisions.

The authors introduce storage losses as a separate variable in a farmer's grain-selling decision model to empirically analyse the impact of storage losses on farmers' grain-selling behaviours. Moreover, the authors analyse the impact of market development on household grain-selling behaviours in China. These findings can help avoid oversupply in the market during the harvest season and alleviate the pressure on the market from the supply and demand imbalance. These results are also beneficial for farmers waiting for a higher price and increasing their income.

The purpose of this study was to estimate on-farm maize storage losses and to empirically analyse the main determinants of maize storage losses in China.

Based on a nationwide survey of 1,196 households in 23 provinces (autonomous regions and municipalities) in China, the authors estimated on-farm maize storage losses and used a fractional logit model to analyse the main determinants of maize storage losses in China.

The results showed that, first, 1.78% of maize was lost during the storage stage in China and that storage loss experienced by smallholders varied across regions. Second, the empirical analysis showed that storage quantities have significant and negative effects on storage losses and that an economy of scale in household storage may exist; the percentage of maize stored for consumption and feed has significant and positive effects on storage losses, which may be related to market requirements and management activities. Third, compared to traditional facilities, silos and warehouses cause lower storage losses, while spraying chemicals and re-drying maize in the storage stage incur higher losses, possibly because to save costs, smallholders conduct loss-reduction activities only when they suffer serious losses, and when taking measures, farmers may sort grains, which also increases losses. Fourth, harvesting maize when it matures is significantly associated with lower storage losses.

To reduce storage losses, first, farmers' awareness of food saving and loss reduction must be increased. This could be achieved through agricultural training and education regarding food-saving practices. Second, the government could provide subsidies or low-interest credits to encourage farmers to use advanced facilities and promote land transfers to realize economies of scale. Finally, the government should accelerate the construction of smart agriculture systems and the timely monitoring of crop growth to suggest farmers to harvest at the appropriate time.

Reducing post-harvest loss (PHL) has become an important means of increasing the food supply and reducing resources use in China. This study provides a complete introduction to household maize storage losses and can therefore help characterize the current state of PHL in China, which is of vital importance to food security and food policy.

The purpose of this paper is to optimize the preventive maintenance based on fault tree (FT)–Bayesian network (BN) reliability for sugarcane harvester machine as a fundamental machine in the sugar industry that must be operated failure-free during a given period of the harvesting process.

To determine machine reliability using the algorithm developed based on mapping FTs into BNs, the common failures of 168 machines were carefully investigated over 12 years (2007–2019). This algorithm was then used to predict the harvester reliability, estimate delays by machine downtimes and their consequences on white sugar production losses that can be reduced by optimizing the preventive maintenance scheduling.

The optimization of preventive maintenance scheduling based on estimated reliability of sugarcane harvester machines using FT–BNs can reduce white sugar production losses, the operation-stopping breakdowns and the downtime costs as a crisis that the sugar industry is facing.

Machine reliability gradually decreased by 31.08% approximately, which resulted in a working time loss of 26% in the 2018–19 harvesting season. In total, the white sugar losses were estimated as 204.17 tons for burnt canes and 114.53 tons for green canes. The losses of the 2018–19 harvesting season have been 11.85 times greater than the first harvesting season. The proposed maintenance interval for critical subsystems including the hydraulic, chopper and base cutter were obtained as 1.815, 1.12 and 1.05 h, respectively.

In this study, a new approach was used to optimize preventive maintenance to reduce delays and their implications upon costs in time, inconvenience and white sugar losses. The FT–BNs algorithm was found a useful tool that was over-fitting of failure occurrence probabilities data for sugarcane harvester machine.

The US Department of Energy has a goal to make ethanol from biomass cost competitive with petroleum by 2012. Feedstock procurement is expected to represent a significant portion of the operating costs for a refinery that produces ethanol from biomass such as switchgrass. Thus, cost‐effective feedstock logistics will be a key factor for the future development of a capital intensive cellulosic ethanol industry. The purpose of this paper is to analyze the cost of various logistic methods of switchgrass production, harvesting, storing, and transportation.

This study applied enterprise budgeting and geographical information system (GIS) software to analyze the costs of three logistic methods of acquiring switchgrass feedstock for a 25 million gallon per year refinery. Procurement methods included traditional large round and rectangular bale harvest and storage systems and satellite preprocessing facilities using field‐chopped material. The analysis evaluated tradeoffs in operating costs, dry matter losses during storage, and investment requirements among the three systems.

Results suggest that the preprocessing system outperformed the conventional bale harvest methods in the delivered costs of switchgrass.

The cost savings in harvest, transportation, and dry matter losses for the preprocessing system offset their extensive capital costs and generated cost advantages over the conventional methods.

The traditional round bale system has a higher overall investment cost, may not be the most cost‐effective way to procure switchgrass feedstock for a refinery, and may limit farmer participation in the feedstock value chain.

GIS methods combined with enterprise budgeting can be useful tools for evaluating investment in feedstock supply chain infrastructure.

The purpose of this paper is to develop a straightforward method to quantify volume and value of postharvest losses in the tomato postharvest value chain in Nepal and estimate the monetary loss shouldered by value chain actors.

The study combines interview data to quantify volume and prices with produce sampling to quantify quality losses, and does this at four nodes of the tomato value chain in Nepal: farmers, collectors, wholesalers, and retailers to estimate volume and value of postharvest losses.

Almost one-fourth of the total tomato harvest weight that enters the value chain is lost before it reaches consumers, and other one-fifth is traded by the value chain actors at reduced price due to quality damage. The total volume of postharvest loss (weight and quality loss) is not the same for all value chain actors and the average monetary loss ranges from 4 percent of gross revenues for farmers to 12 percent for wholesalers.

A standard method to account for both physical weight losses and quality losses of horticultural produce is lacking in estimates of the monetary value of postharvest losses for horticultural crops. Knowing such losses is essential for postharvest technology generation, promotion, and adoption. This study provides a framework that can be adopted and improved in future loss assessment studies for estimating the volume and value of postharvest losses in a horticultural value chain.

The uniqueness of the method used in this study is that it combines interview data to estimate price and volume with produce sampling to quantify quality losses, and does this at four nodes of the value chain: farmers, collectors, wholesalers, and retailers. This method could become a standard approach for assessment of postharvest weight and quality losses and to estimate the monetary value of total postharvest losses in the value chain for horticultural crops.

Food Industries have to cater a plethora of consumers having variety of tastes. For sustaining in such environment companies create their unique selling point and big data helps them to analyze market situation for such purpose. In this book chapter, the supply chain of fruits and vegetables and the post-harvest losses encountered at each stage in absence of data analytics is discussed. This can be an opportunity for the food industries to reduce food loss and gain better returns on investment by going for a digital transformation. Companies combine big data with technologies like machine learning and artificial intelligence to get faster and more personalized experiences. This chapter includes comparative case studies of food and retail sector for better understanding of the outcome.

The literature review of post-harvest supply chain (PHSC) losses is carried out and analyzed in this paper followed by bibliometric analysis of the literature.

The literature survey is performed across various dimensions such as PHSC losses, PHSC risks and PHSC sustainability (waste management and waste reduction). One hundred thirty research articles during the period of 1989–2020 were considered for the review.

The PHSC losses have been identified in this literature survey. The calculation and mitigation strategies stated by various researchers in the literature are addressed. The important loss mitigation dynamics are also presented to reduce the PHSC losses and to improve food availability.

The major focus is given on the PHSC of agriculture produces. However, research articles from fish and meat supply chain are excluded as they follow a different perishability curve.

The current work will add value to the agriculture supply chain literature, provide a platform for PHSC losses and provide assistance/guideline toward loss calculation, loss mitigation, improved rural employability, improved rural entrepreneurship and improved revenue generation.

The performed research will assist the researchers, entrepreneurs and farmers to understand the current scenario of food wastage at different stages of the supply chain better. It will provide the guidelines for calculation and mitigation of various stated PHSC losses. This study will be helpful to enhance food availability and food security in post-coronavirus crisis.

The paper explores and highlights PHSC loss calculations and mitigation strategies to identify the postharvest loss situation and better utilization of fresh produces.