Purpose: International agencies have advocated that monitoring food security and world food resources are necessary to meet the needs of growing populations and to minimize postHARVEST LOSSES. This paper focuses on the biochemical and physiological bases of changes that causes post-HARVEST LOSSES and ways to mitigate them. By controlling these metabolic changes, some degree of preservation is possible. Findings: PostHARVEST LOSSES are 30-50% in developing countries due to energy crisis and lack of proper handling procedures and refrigeration; in contrast to less than 15% in developed countries. Highly perishable commodities like fruits and vegetables are living entities which are characterized by life evolving activities like respiration, transpiration, ripening and metabolic changes. Various compositional changes, such as chlorophyll degradation, softening, and ascorbic acid LOSSES can result in short shelf life. Total 63 species of vegetables are grown in Pakistan but onions, potatoes, tomatoes, garlic, green chilies, coriander, spinach, pumpkin and okra are mostly grown and consumed. Limitations: In Pakistan due to energy crisis and economic constraints no cold food chains/transport is available as a result of which fresh produce endured post-HARVEST LOSSES.  There is a need to use production technologies supplemented with postHARVEST techniques to mitigate postHARVEST LOSSES.Directions for Future Research: Many new technologically viable preservation techniques like modified atmosphere packaging and controlled atmosphere storage should come into existence due to increased health consciousness, increased purchasing power and an increase in percentage of postHARVEST LOSSES (25-80% fresh produce) which could be applied with such economic constraints.

IntroductionIn conventional combine HARVESTers, wheat chaff is typically removed from the end of the machine and deposited on the field surface. Depending on the wheat cultivar, cultivation method, and growing conditions, the amount of chaff produced can range from 0.8 to 1.5 times the amount of grain HARVESTed per hectare (Tavakoli, 2012). On average, this translates to an annual production of approximately 14 million tons of chaff, which is valued at around $240000000 based on regional prices in 2018-2019 ($1000 per kilogram). If collected, these chaff residues could be used as animal feed for livestock. Additionally, the long stems protruding from the back of conventional combine HARVESTers can interfere with subsequent cultivation efforts. Chaff combine HARVESTers have a similar structure to conventional machines, but feature a modified end that includes a tank and blower for collecting and depositing crushed chaff. Apart from the threshing unit, all other components of the HARVESTer remain unchanged.Materials and MethodsThis study was conducted in 2019 in dryland wheat fields to determine the performance of Chaff combine HARVESTers in Kurdistan province. The study used 15 combine HARVESTers, including John Deere models equipped with chaff threshers from Shiraz, Bookan, and Hamedan, as well as the Hamedan Barzegar specific chaff collector combine. These combines were evaluated and compared based on natural LOSSES, head and chaff storage LOSSES, field capacity, purity percentage, and yield in field conditions in Kurdistan province. The total number of combines evaluated was 15, using a completely randomized design. Among these, 33% belonged to Shiraz company (5 samples), 33% to Bookan (5 samples), 20% to Hamedan (3 samples), and 14% to Hamedan Barzegar (2 samples). Sampling included measurement of natural LOSSES, header LOSSES, threshing tank LOSSES (LOSSES of the threshing unit, separating unit, and cleaning unit), and quality LOSSES (broken grains and impurities) in the combine tank.Results and DiscussionThe results showed that the average yield, natural loss, and combine loss were 1,698.14 kg.ha-1, 2.39%, and 4.92%, respectively. In terms of the loss rates in different parts of the combine, 43.49% was related to the chaff storage of the combine, and 56.50% was related to the combine head.The natural loss rate in this province was 2.39%. The total combine loss was 5.18%, with 40.44% of that related to chaff storage and the rest related to the combine head. The results also showed a significant difference between the treatments in terms of field capacity, chaff storage loss, and purity percentage at a probability level of 5%.The total loss of the Hamedan Barzegar combine was 6.67%, which was higher than the other combines. The chaff storage loss of the Shiraz, Bookan, Hamedan, and Hamedan Barzegar combines were 0.87%, 2.64%, 0.78%, and 4.28%, respectively, showing a significant difference at a 5% level. There was also a significant difference between the treatments in terms of total grain loss.Based on these results, it is recommended to use the Hamedan, Bookan, Shiraz, and Hamedan Barzegar combines, with total LOSSES of 4.33%, 4.33%, 4.52%, and 6.56%, respectively.ConclusionThe average purity of HARVESTed grains was 96.62%, and there was no significant difference between the combine HARVESTers in this regard.There was a significant difference between the combines in terms of field capacity at a probability level of 5%. The field capacity of the Bookan, Hamedan Barzegar, Hamedan, and Shiraz combine HARVESTers were 0.83, 0.87, 0.83, and 0.73 hectares per hour, respectively.In Kurdistan province, the average grain combine loss in dryland wheat HARVESTing with chaff combine HARVESTers was 4.92%, which is higher than in other provinces.The loss in the chaff tank of the Shiraz, Bookan, Hamedan, and Hamedan Barzegar combine HARVESTers was 0.87%, 2.64%, 0.78%, and 4.28%, respectively. Regardless of head LOSSES, the loss in the Hamedan combine was less than other combine HARVESTers.The total LOSSES of the Hamedan Barzegar, Bookan, Shiraz, and Hamedan combine HARVESTers were 6.56%, 4.32%, 4.52%, and 4.30%, respectively, with the Hamedan Barzegar and Hamedan combine HARVESTers having the highest and lowest LOSSES, respectively.Based on the results obtained from this study, using the Hamedan combine is recommended in the dryland conditions of Kurdistan due to its low LOSSES, high purity, and field capacity.AcknowledgementThanks to the Agricultural Jihad Organization of Kurdistan Province, specifically the deputy of the Plant Production and Mechanized Technologies Department, for their assistance and cooperation in the implementation of the project.

In this study, two transport methods for tomato (room temperature and refrigerated transport) as well as post-HARVEST packaging treatments (thin polyethylene packaging bags, thick polyethylene packaging bags, use of 1%calcium chloride, use of absorbent paper in the box, and control) were examined at four stages of post-HARVEST consumer chain transport. These stages included: (1) Farm, after HARVESTing and putting in boxes, (2) Transport, after transferring products for wholesale and during discharging, (3) Wholesale, after discharging and when selling to local retailers usually 24-36 hours after HARVESTing time, and (4) Retail stores (2 days after HARVESTing time at most). The total acid level, vitamin C, and lycopene of the fruit differed across different farms, different transportation conditions, post-HARVEST treatments, and the four stages of product transport. The total percentage of unacceptable fruits was significant in post-HARVEST treatments and at different stages of transport. Among post-HARVEST treatments, packaging with high-density plastic and absorbent paper with 7. 94 and 12. 16% of weight loss, respectively, claimed the minimum and maximum physiological loss in fruit weight. The minimum post-HARVEST loss (4. 21 percent) was related to high-density plastic packaging.

The present study investigated the thermal properties of canola pods (thermal conductivity, specific heat, and thermal diffusivity), canola LOSSES (natural LOSSES, platform LOSSES, and total combine HARVESTer LOSSES), and unbroken pods in three common canola varieties cultivated in the North of Iran (Hyola 420, Hyola 401, and Hyola 50) at three times of pre-HARVEST, HARVEST, and post-HARVEST. Furthermore, the relation between the thermal properties of canola pods and the amounts of LOSSES during HARVEST was studied. Thermal conductivity coefficient, specific heat, and thermal diffusivity were determined using line heat source, mixture method, and calculation methods, respectively. Seed LOSSES were calculated, using a built grain collector. The results revealed that adjustments, variety, and sampling time had significant effects on thermal conductivity and specific heat of canola varieties at the probability level of 1%. The effect of the interaction between variety and time on thermal conductivity, specific heat, and thermal diffusivity was considerable at the probability levels of 1% and 5%, respectively. Furthermore, the effects of canola varieties and HARVEST time on natural LOSSES, total combine HARVESTer LOSSES, as well as unbroken pods were substantial at 1% probability. In addition, a notable relation was observed between thermal conductivity coefficient and platform LOSSES at 5% and unbroken pods at 1%. However, unbroken pods indicated a substantial relation with specific heat and thermal diffusivity at 1%.

Pre-HARVEST sprouting is one of the main factors that reducing the rice grain yield and quality in humid weather condition with raining at HARVEST time. Thirty four local and improved rice cultivars were evaluated for pre-HARVEST sprouting and some related characteristics to this trait at in vivo condition. Analysis of variance results indicated that the differences between rice genotypes were significant for pre-HARVEST sprouting and some other traits of grain. Genotypic and phenotypic correlation coefficients of pre-HARVEST sprouting with alpha amylase activity were positive, this result showed that the alpha amylase activity had significant effect on pre-HARVEST sprouting. All rice cultivars classified into three groups by cluster analysis using ward method. First group included 24 cultivars and second and third groups included 4 and 6 cultivars respectively. In total, cultivars of the first and third groups had the lowest of pre-HARVEST sprouting rate, so they were resistant to germination before HARVEST and the cultivars of second group had the highest of pre-HARVEST sprouting rate, so they were sensitive to germination before HARVEST. Results of this experiment showed some local rice cultivars such as Domsefid and Hashemi, with high resistance to pre-HARVEST sprouting could potentially be used in breeding programms.

Introduction: Corn HARVEST LOSSES are imposed by several factors, the most important of which is HARVESTing-time. Since the HARVESTing-time is coincident with the rainy season, it is necessary to appropriately estimate the corn HARVEST time to avoid HARVESTing LOSSES and losing the next cultivation. Accordingly, in the current research, the effect of HARVESTing-time on corn LOSSES during the month and the day has been into consideration. An expert fuzzy system was designed to predict the best HARVEST time as it operates based on the LOSSES amounts which are measured in processing and collection units into the combine, and LOSSES due to the humidity percentage. Materials and Methods: In this paper, corn HARVEST LOSSES in a John Deere Combine, Model 1165, was studied in a different climatic circumstance in Moghan region. Moreover, a split plot experiment in a completely randomized block design was conducted with three replications. The LOSSES data were collected from the processing and collection units of the combine HARVESTer on the November 5th, 8th and 11th, 2017, in three different daily times of 8-10, 11-13 and 14-16 with three replications. The Mamdani fuzzy inference system with singleton fuzzifire and center average defuzzifire was used to develop a fuzzy expert system. In the designed expert system, the LOSSES percentage in the processing and collection units and the humidity percentage were considered as system inputs and optimal HARVESTing time was used as the system output. "Low, Very low, high and very high" and " Best, Suitable, Unfit, and Worst" were four groups of linguistic variables for input and output parameters, respectively. These variables follow the triangular and trapezoidal membership functions. The number of 64 fuzzy rules were considered and introduced into the fuzzy system by experts, experienced farmers, and combiners. Furthermore, the same field data (measured data) were applied to evaluate the designed system, so that the predicted value was accounted as the system output. Results and Discussion: Analysis of variance showed that there was a significant difference between the HARVESTing dates at the 0. 05 probability level and significant difference between the HARVESTing times of a day at the 0. 01 probability level. It can be concluded that the HARVEST dates and HARVEST times of a day were very effective in the number of corn LOSSES, but the interaction effects were not significant. The results appeared that the lowest LOSSES were 10. 05% on November 8th, 2017, at 14-16 p. m., and the highest LOSSES were 12. 88% on November 11th, 2017, at 8-10 a. m. The amount of LOSSES was increased due to the higher air humidity and lower temperature. In the fuzzy simulation model, the suitable HARVESTing-time can be predicted based on the LOSSES quantities in the processing and collection units and the humidity percentage. The results showed that the predicted values for HARVESTing-times, by a designed fuzzy system, were completely matched with measured values in this study. The coefficient of determination (R2) was 0. 980 between measured and predicted HARVESTing times. This coefficient demonstrated that the developed fuzzy logic system was suitable for prediction of HARVESTing time in the studied area. Conclusions: The experimental observations in the field and data analysis showed that in the corn HARVESTing in the Moghan region, the humidity level, date, and HARVESTing-time were the most effective factors in the HARVESTing LOSSES. In this paper, based on measured data from a small farm and implementation of the expert fuzzy system, the most suitable HARVEST date was set on November 8th at 14-16 p. m, at 21-24° C and relative humidity of 44%-53% to have 10. 5% LOSSES which has been confirmed by the lowest LOSSES observed in the corn plan (10%). Moreover, the high value of the determination coefficient demonstrates a high correlation between measured and predicted data.

South Khorasan province is the largest producer of barberries in Iran and the world, with 14000 hectares and 98.7% of the national barberry production. Knowledge of HARVEST timing, proper HARVESTing and drying methods increase the quality and improve the status of barberries on the domestic and global markets. This study investigated the effect of HARVESTing method (striking, picking up bunches, cutting branches), HARVESTing time (mid-October, late October and mid-November) and drying method (drying in shade, drying in sun, industrial dryer) on product quality. The results showed that the mass density of dried barberries is influenced by HARVESTing and drying methods. Cutting branches caused the lowest mass density (214.86 kg/m3). The lowest colorimetric quality samples were produced by drying in the sun after the first HARVEST time and the best quality was produced by drying in the shade after the third HARVEST time.Results showed that drying in the sun and industrial drying methods reduced the color quality of the product. The results of sensory tests confirmed that the best quality samples were HARVESTed by picking up bunches at the second HARVEST time and shade drying. The lowest assessment was for samples HARVESTed by striking at the first HARVEST time and sun drying.