Modeling
S. Sharifi; N. Hafezi; M. H. Aghkhani
Abstract
IntroductionEfficient use of energy in paddy production can lower greenhouse gas emissions, safeguard agricultural ecosystems, and promote the growth of sustainable agriculture. Meanwhile, intelligent agriculture has come to the aid of farmers and policy-makers by harnessing cutting-edge technologies, ...
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IntroductionEfficient use of energy in paddy production can lower greenhouse gas emissions, safeguard agricultural ecosystems, and promote the growth of sustainable agriculture. Meanwhile, intelligent agriculture has come to the aid of farmers and policy-makers by harnessing cutting-edge technologies, which will lead to sustainable welfare and the comfort of human society in the present and the future. Therefore, this study aimed to analyze energy consumption and production, as well as model and optimize the yield of two paddy cultivars using Artificial Bee Colony (ABC) and Genetic Algorithms (GA).Materials and MethodsExtensive research data was collected by thoroughly examining documentary and library resources, as well as conducting face-to-face questionnaires with 120 paddy farmers and farm owners in Rezvanshahr city, located in the province of Guilan, Iran, during the 2019-2020 production year. The farms consisted of 80 high-grading and 40 high-yielding paddies. The independent variables were machinery, diesel and gasoline fuels, electricity, seed, compost and straw, biocides, fertilizers, and labor. The dependent variable was paddy yield per hectare [of the farm area]. In the first step, energy consumption and production were calculated by multiplying the variables by their corresponding coefficients. In the second step, all the variables that maximize paddy yield were entered into MATLAB software. An artificial bee colony (ABC) algorithm with a novel and straightforward elitism structure was utilized to enhance the fitness function of the genetic algorithm (GA). The Sphere, Repmat, and Unifrnd functions were employed to determine the objective function, define the position of the bee colony, and quantify the position of the bee colony, respectively. In each generation, 900 new solutions were created, and the algorithm iterated 200 times. For the genetic algorithm, the population was defined as a double vector with a size of 100.Results and DiscussionThe findings revealed that the Hashemi (high-grading) paddy cultivar had an average energy consumption and production of 55.973 and 30.742 GJ·ha-1, respectively. The Jamshidi (high-yielding) paddy cultivar had an average energy consumption of 54.796 GJ·ha-1 and double the energy production of the Hashemi at 62.522 GJ·ha-1. In both cultivars, agricultural machinery consumed the highest amount of energy, while straw consumed the lowest amount. The average energy consumption of tractors in the Hashemi and Jamshidi cultivars was 25.111 and 25.865 GJ·ha-1, respectively, accounting for 44.862% and 47.202% of the total average consumed energy. This undoubtedly demonstrates the significant effect of this input and reflects the operators' skill and experiential knowledge. The evaluation indexes, including R², RMSE, MAPE, and EF, as well as statistical comparisons such as mean, STD, and distribution, consistently demonstrated that the ABC algorithm provided the essential conditions for the fitness function. The results of the bee-genetic algorithm optimization revealed that the majority of the consumed resources could be effectively managed on the farm to closely match optimal conditions. Through this optimization, energy consumption in the Hashemi and Jamshidi cultivars was reduced by 53.96% and 39.41%, respectively.ConclusionGiven its impressive performance and potential for minimizing energy consumption, the ABC-GA algorithm offers an opportunity for policymakers in energy resource management and rice industry managers to develop innovative strategies for significantly reducing energy usage in rice production. This approach could lead to more sustainable and efficient practices in the agricultural sector.
Post-harvest technologies
S. Sharifi; M. H. Aghkhani; A. Rohani
Abstract
IntroductionOn the field and in the paddy milling factory dryer losses have always been challenging issues in the rice industry. Different forms of losses in brown rice may occur depending on the field and factory conditions. To reduce the losses, proper management during pre-harvest, harvesting, and ...
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IntroductionOn the field and in the paddy milling factory dryer losses have always been challenging issues in the rice industry. Different forms of losses in brown rice may occur depending on the field and factory conditions. To reduce the losses, proper management during pre-harvest, harvesting, and post-harvest operations is essential. In this study, different on-field drying and tempering methods were investigated to detect different forms of brown rice losses.Materials and MethodsThe present study was conducted on the most common Hashemi paddy variety during the 2019-2020 season in Talesh, Rezvanshahr, and Masal cities in the Guilan province, Iran with 0.2 hectares and 5 paddy milling factory dryers. On the fields, the method and date of tillage, irrigation, and transplanting used in all experimental units were the same. Moreover, the same amount of fertilizer and similar spraying methods were used across all experiments. For the pre-drying process on the fields, the following three pre-drying methods were applied on the harvest day: A1) The paddies were spread on the cut stems for insolating, A2) The paddies were stacked and stored after being placed on the cut stems for 5h, and A3) The paddies were covered with plastic wrap and stored after 5h of insolating. The first method (A1) is the most common in the area and was chosen as the control treatment. For the second step of the process, the time interval between the on-field pre-drying and threshing was considered: B1) 14 to 19h post-harvest; B2) 20 to 24h post-harvest, and B3) 25 to 29h post-harvest. Afterward, methods A1 to A3 were combined with methods B1 to B3 and feed into an axial flow-thresher at 10 kg min-1, 550 rpm PTO, and two levels of moisture content at 19 and 26 percent (% w.b). The third process was two-stage or three-stage tempering for 10 or 15 hours resulting in four levels (C1 to C4) and was done in the conventional batch type dryer under temperatures of 40 and 50 ˚C and airspeeds of 0.5 and 0.8 m s-1 in paddy milling factories. At the end of each process, a 100g sample was oven-dried for 48h and a microscope achromatic objective 40x was used to detect incomplete horizontal or vertical cracks, tortoise pattern cracks, and immature and chalky grains. The equilibrium moisture content was determined to be 7.3 percent. Losses properties were analyzed using a completely randomized factorial design with a randomized block followed by Tukey's HSD test at the 5% probability and comparisons among the three replications were made.Results and DiscussionResults demonstrated that the stack and plastic drying methods significantly increased the percentage of losses. In the plastic drying method, the percentage of chalky grains and tortoise pattern cracks was higher than other forms of loss. In the first process, irrespective of the pre-drying method, the losses were reduced at a lower level of moisture content. At the end of the first stage, losses in the spreading method were significantly lower at 19% moisture content. Threshing the plastic-wrapped paddies after 14 to 19 hours at 19% moisture content resulted in the maximum threshing loss of 8.446% and over half of the grains were chalky or had tortoise pattern cracks. The threshing loss was halved (4.443%) for paddies threshed 25 to 29h after spreading at a moisture content of 26%. The mean of losses in the second step of the process were 7.229, 5.585, and 5.156% for the time interval between the on-field pre-drying and threshing of 14 to 19h, 20 to 24h, and 25 to 29h, respectively. In the last step of the process in paddy milling factory dryers, there was no significant difference in the minimum percent of losses between 10 and 15 hours of three-stage tempering at 40 °C and with 0.5 m s-1 airspeed. Furthermore, maximum total losses with the most incomplete horizontal and vertical cracks occurred in the two-stage 10h tempering at 50 °C and with 0.5 and 0.8 m s-1 airspeed.ConclusionFood security has always been a critical matter in developing countries. Furthermore, identifying the source of losses in the fields and the factories is one way to reduce losses and achieve food security. Stacking or wrapping the paddies in plastic after pre-drying on the fields for 5h is not recommended in terms of its effect on increasing the percentage of brown rice losses. Additionally, due to the importance of factory dryer scheduling in the management of the losses, it is recommended to use a three-stage 10h tempering at 40 °C and with 0.5 m s-1 airspeed.
