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Recent Innovations in Post-harvest Preservation and Protection of Agricultural Products—Series...
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Recent Innovations in Post-harvest Preservation and Protection of Agricultural Products—Series II

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Product Quality and Safety".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 8772

Special Issue Editor

Prof. Dr. Dirk E. Maier

E-Mail Website
Guest Editor
Department of Agricultural & Biosystems Engineering, Iowa State University, Ames, IA 50011, USA
Interests: food and nutrition security; post-harvest engineering; grain quality; non-chemical protection technologies; post-harvest loss reduction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The global food supply chain relies on engineered systems, operational practices, and logistics to preserve, protect, process, and deliver agricultural crops along complex supply lines from farmers in low-, middle-, and high-income countries to markets around the world. Food and nutrition security is compromised by post-harvest losses (and food waste) that have been estimated to be as high as 20% in durable and 40% in perishable crops. Preserving crops using technologies and practices such as timely harvesting, evaporative cooling, cold and frozen storage, drying, dehydrating, and protecting crops using technologies and practices such as damage-less handling, controlled- and modified-atmosphere storage, non-chemical heat and gas treatment, plant-derived protective films for individual fruits and vegetables, and improved packaging containers are critical to preserving nutrients, improving livelihoods, and realizing an efficient food system.

This second Special Issue aims to cover recent progress and innovations in science, technology, engineering, operational practices, and logistics related to the post-harvest preservation and protection of durable and perishable agricultural crops. It seeks contributions that improve effectiveness, efficiency, reliability and sustainability in post-harvest handling of crops from field to end use that preserve product quality and result in foods and feeds which are nutritious and safe for human and animal consumption.

Key topics in this Special Issue include but are not limited to the following:

  • Novel methods to preserve end-use quality, shelf-life, and nutritional value of harvested durable and perishable crops;
  • Novel methods to protect stored crops from insect pests, fungi, and other spoilage agents;
  • Novel sensors and measurement technologies to quantify end-use quality, shelf-life, and nutritional value of harvested crops;
  • Automation, monitoring, and control of crop storage technologies and systems;
  • Best practices to reduce and prevent post-harvest losses;
  • Techno-economic analysis and life-cycle assessment of engineered systems, operational practices, and logistics to preserve, protect, process, and deliver agricultural crops.

Prof. Dr. Dirk E. Maier
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • post-harvest drying, handling, storage
  • stored product protection
  • crop shelf-life preservation
  • post-harvest loss reduction
  • crop end-use quality sensors
  • engineered crop storage systems
  • crop delivery logistics

Published Papers (6 papers)

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Research

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14 pages, 4279 KiB  
Article
Tracking Food Supply Chain Postharvest Losses on a Global Scale: The Development of the Postharvest Loss Information System
by Thiago Guilherme Péra, Fernando Vinícius da Rocha and José Vicente Caixeta Filho
Agriculture 2023, 13(10), 1990; https://doi.org/10.3390/agriculture13101990 - 13 Oct 2023
Viewed by 1350
Abstract
Reducing food losses presents an opportunity to enhance food security, minimize waste, and improve profitability within the production sector. Creating awareness among various stakeholders in the value chain about the significance of reducing postharvest losses is a fundamental step in this discussion. This [...] Read more.
Reducing food losses presents an opportunity to enhance food security, minimize waste, and improve profitability within the production sector. Creating awareness among various stakeholders in the value chain about the significance of reducing postharvest losses is a fundamental step in this discussion. This article addresses the Postharvest Loss Information System (SIPPOC) development and applicability. SIPPOC encompasses tools designed to facilitate understanding food loss occurrences across different supply chain segments. The article provides insights into the tools incorporated within the information system and describes its historical background and protocol for database updates. In essence, SIPPOC enables the analysis of food loss throughout diverse logistical stages, thereby aiding in identifying critical points and implementing targeted actions for loss reduction. Drawing on SIPPOC data, the article further examines losses within the logistics chain by comparing potato, tomato, and mango agricultural productions. Full article
(This article belongs to the Special Issue Recent Innovations in Post-harvest Preservation and Protection of Agricultural Products—Series II)
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27 pages, 4863 KiB  
Article
Identifying Critical Drivers of Mango, Tomato, and Maize Postharvest Losses (PHL) in Low-Income Countries and Predicting Their Impact
by Hory Chikez, Dirk Maier, Sigurdur Olafsson and Steve Sonka
Agriculture 2023, 13(10), 1912; https://doi.org/10.3390/agriculture13101912 - 29 Sep 2023
Viewed by 755
Abstract
Several studies have identified a host of factors to be considered when attempting to reduce food postharvest loss (PHL). However, very few studies have ranked those factors by their importance in driving PHL. This study used the Random Forest model to rank the [...] Read more.
Several studies have identified a host of factors to be considered when attempting to reduce food postharvest loss (PHL). However, very few studies have ranked those factors by their importance in driving PHL. This study used the Random Forest model to rank the critical drivers of PHL in maize, mango, and tomato, cultivated in Tanzania, Kenya, and Nigeria, respectively. The study then predicted the maize, mango, and tomato PHLs by changing the levels of the most critical drivers of PHL and the number of farmers at each level. The results indicate that the most critical drivers of PHL consist of pre-harvest and harvest variables in the field, such as the quantity of maize harvested in the maize value chain, the method used to know when to begin mango harvest, and the type of pest that attacks plants in the tomato value chain. Furthermore, changes in the levels of a critical driver and changes in the number of smallholder farmers at a given level both have an impact on PHL, although the impact of the former is much higher than the latter. This study also revealed that the critical drivers of PHL can be categorized as either passive and difficult to manipulate, such as the geographic area within which a smallholder farmer lives, or active and easier to control, such as services provided by the Rockefeller Foundation YieldWise Initiative. Moreover, the affiliation of smallholder farmers to the YieldWise Initiative and a smallholder farmer’s geographic location are ubiquitous critical drivers across all three value chains. Finally, an online dashboard was created to allow users to explore further the relationship between several critical drivers, the PHL of each crop, and a desired number of smallholder farmers. Full article
(This article belongs to the Special Issue Recent Innovations in Post-harvest Preservation and Protection of Agricultural Products—Series II)
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31 pages, 6412 KiB  
Article
A Back Propagation Neural Network Model for Postharvest Blueberry Shelf-Life Prediction Based on Feature Selection and Dung Beetle Optimizer
by Runze Zhang, Yujie Zhu, Zhongshen Liu, Guohong Feng, Pengfei Diao, Hongen Wang, Shenghong Fu, Shuo Lv and Chen Zhang
Agriculture 2023, 13(9), 1784; https://doi.org/10.3390/agriculture13091784 - 09 Sep 2023
Viewed by 1193
Abstract
(1) Background: Traditional kinetic-based shelf-life prediction models have low fitting accuracy and inaccurate prediction results for blueberries. Therefore, this study aimed to develop a blueberry shelf-life prediction method based on a back propagation neural network (BPNN) optimized by the dung beetle optimizer using [...] Read more.
(1) Background: Traditional kinetic-based shelf-life prediction models have low fitting accuracy and inaccurate prediction results for blueberries. Therefore, this study aimed to develop a blueberry shelf-life prediction method based on a back propagation neural network (BPNN) optimized by the dung beetle optimizer using an elite pool strategy and a Gaussian distribution estimation strategy (GDEDBO); (2) Methods: The “Liberty” blueberry cultivar was used as the research object, and 23 quality indicators, including color parameters, weight loss rate, decay rate, and texture parameters, were measured under storage temperatures of 0, 4, and 25 °C. Based on the maximum relevance minimum redundancy (MRMR) algorithm, seven key influencing factors of shelf life were selected as the input parameters of the model, and then the MRMR-GDEDBO-BPNN prediction model was established; (3) Results: the results showed that the model outperformed the baseline model at all three temperatures, with strong generalization ability, high prediction accuracy, and reliability; and (4) Conclusions: this study provided a theoretical basis for the shelf-life determination of blueberries under different storage temperatures and offered technical support for the prediction of remaining shelf life. Full article
(This article belongs to the Special Issue Recent Innovations in Post-harvest Preservation and Protection of Agricultural Products—Series II)
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20 pages, 932 KiB  
Article
The YieldWise Approach to Post-Harvest Loss Reduction: Creating Market-Driven Supply Chains to Support Sustained Technology Adoption
by Steven Sonka, Hyeonsuh Lee and Sonali Shah
Agriculture 2023, 13(4), 910; https://doi.org/10.3390/agriculture13040910 - 21 Apr 2023
Cited by 1 | Viewed by 1839
Abstract
Excessively high levels of post-harvest loss often are a feature of agricultural systems dominated by small-holder farmers. However, this situation is something of a paradox, as technologies exist that have been shown in field demonstrations to substantially reduce post-harvest loss. What explains this [...] Read more.
Excessively high levels of post-harvest loss often are a feature of agricultural systems dominated by small-holder farmers. However, this situation is something of a paradox, as technologies exist that have been shown in field demonstrations to substantially reduce post-harvest loss. What explains this paradox? Building on insights derived from the Rockefeller Foundation’s YieldWise Initiative, this article proposes that while reducing post-harvest loss generally does require technology adoption by small-holder farmers, market-driven supply chains are essential to the sustained use of those technologies. We illustrate this approach using in-depth interview data collected from the YieldWise participants belonging to the Iringa Hope Cooperative in Tanzania. Data on the benefits and challenges of such an approach are provided from the perspective of the small-holder farmer. In addition, we model the economic benefits associated with this approach. Full article
(This article belongs to the Special Issue Recent Innovations in Post-harvest Preservation and Protection of Agricultural Products—Series II)
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16 pages, 2377 KiB  
Article
Modeling and Application of Temporal Correlation of Grain Temperature during Grain Storage
by Hongwei Cui, Qu Zhang, Wenfu Wu, Haolei Zhang, Jiangtao Ji and Hao Ma
Agriculture 2022, 12(11), 1883; https://doi.org/10.3390/agriculture12111883 - 09 Nov 2022
Cited by 2 | Viewed by 1246
Abstract
Temperature measurement system malfunction and sensor failure in grain storage warehouses can lead to missing grain temperature data on some days. Missing data is not conducive to the monitoring of grain storage conditions. This paper establishes mathematical models of temporal correlation coefficients of [...] Read more.
Temperature measurement system malfunction and sensor failure in grain storage warehouses can lead to missing grain temperature data on some days. Missing data is not conducive to the monitoring of grain storage conditions. This paper establishes mathematical models of temporal correlation coefficients of grain temperature and storage time in different planes, and analyzes the influence of storage state change on grain temperature correlation. The historical grain situation data for about one year were selected from 27 flat grain storage warehouses distributed in the second to seventh grain storage ecological zones in China. In addition, correlation coefficients of grain temperature were then calculated on the XOY, XOZ and YOZ planes of each warehouse. During this process, the time interval included 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days, meaning that the correlation coefficients between the grain temperature on the day and the grain temperature after storage for 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days were calculated. Next, the correlation coefficients from the same time intervals and planes in each warehouse were sequentially connected to form arrays of correlation coefficients. Then, the 3σ-threshold setting methods and DBSCAN (density-based spatial clustering of applications with noise) method were used to analyze the correlation coefficients those arrays. According to the results, we set the correlation coefficient thresholds for each plane (XOY, XOZ and YOZ planes) at each time interval. The models were then established regarding the correlation coefficient thresholds and storage time intervals. Subsequently, the sum of squares for error (SSE), coefficient of determination (R2), and root mean square error (RMSE) were chosen to evaluate the models, with the results showing that the effect of the model established by the threshold set by the 3σ-setting method, with SSE, R2 and RMSE of 0.056, 0.9771 and 0.0748, respectively, was better than the model established using the DBSCAN method. Finally, the correlation coefficients of grain temperatures with empty warehouse, new grain addition, aeration and self-heating were analyzed. The results show that the four modes in a certain time interval (e.g., 30 days) does not meet the correlation coefficient threshold during normal storage. The result can provide a theoretical basis for grain storage condition detection when grain temperature data is intermittently missing. Full article
(This article belongs to the Special Issue Recent Innovations in Post-harvest Preservation and Protection of Agricultural Products—Series II)
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Review

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14 pages, 310 KiB  
Review
The Mechanism of Drug Carryover in Feed Manufacturing as a Function of Drug Properties and Equipment Design—A Brief Review
by Esther Y. Akoto and Dirk E. Maier
Agriculture 2023, 13(9), 1834; https://doi.org/10.3390/agriculture13091834 - 19 Sep 2023
Viewed by 1009
Abstract
This paper thoroughly reviews the mechanism of veterinary drug carryover in feed manufacturing facilities, factors resulting in varying concentrations of drug carryover in processing equipment, the impact of chemical and physical properties of drugs, and the effect of equipment type and design. The [...] Read more.
This paper thoroughly reviews the mechanism of veterinary drug carryover in feed manufacturing facilities, factors resulting in varying concentrations of drug carryover in processing equipment, the impact of chemical and physical properties of drugs, and the effect of equipment type and design. The Google Scholar database (from 1998 to 2023) was searched with words and phrases such as drug carryover, feed manufacturing, equipment cleaning and validation, food allergen control, sources of drug carryover, and process parameters in drug carryover. Some papers were from the Iowa State University Library database and PubMed. Drug carryover is a function of ingredients, nature of drugs, equipment type, process parameters, and cleaning procedures. The gaps are the lack of commercial feed mills data on the role and interaction of nanomaterials, molasses, equipment type, and process parameters in drug carryover in animal feed. Modification of process parameters, e.g., airflow in bucket elevators and the interaction of feed ingredients, composition, equipment type, and design, need to be investigated in the commercial setting to address drug carryover. Rhetorically, can big data facilitate the standardization of cleaning procedures at feed mills? The findings can result in drug carryover prevention/control in animal feed and animal-based human food. Full article
(This article belongs to the Special Issue Recent Innovations in Post-harvest Preservation and Protection of Agricultural Products—Series II)
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