The grain moisture content at harvest time is a key factor that limits harvesting windows. The present study aimed to develop a new methodology to predict wheat moisture content by using multi-layer perceptron (MLP) and support vector regression (SVR) techniques. Five input variables included the number of days after sowing, air temperature, air relative humidity, wind speed on an hourly basis, and precipitation on a 6-hour basis. The study area was Sari County located in the north of Iran. Data were collected from field experiments in two crop years (2016/17 and 2017/18). The results indicated that the developed MLP model outperformed the SVR model in determining wheat moisture content by R2 and RMSE value of 0.92 and 2.09% (wet basis) against 0.79 and 3.09%, respectively. In conclusion, the developed MLP model can be considered a useful method to estimate wheat moisture content at harvest time.
Mathematical modeling of economic indices is a challenging topic in crop production systems. The present study aimed to model the economic indices of mechanized and semi-mechanized rainfed wheat production systems using various multiple linear regression models. The study area was Behshahr County located in the east of Mazandaran Province, Northern Iran. The statistical population included all wheat producers in Behshahr County in 2016/17 crop year. Five input variables were human labor, machinery, diesel fuel, chemical (chemical fertilizers and chemical pesticides) costs, and the income was considered to be the output. The results showed that the cost of wheat production in the semi-mechanized system was higher than that of the mechanized system. In both systems, the highest cost was related to agricultural machinery input. Moreover, seed cost was lower in the mechanized system than that of the semi-mechanized system. The net return indicator was 993.68 $ ha−1 and 626.71 $ ha−1 for the mechanized and semi-mechanized systems, respectively. The average benefit to cost ratio was 3.46 and 2.40 for the mechanized and semi-mechanized systems, respectively, demonstrating the greater profitability of the mechanized system. The results of the evaluation of five types of regression models including the Cobb-Douglas, linear, 2FI, quadratic and pure-quadratic for the mechanized and semi-mechanized production systems indicated that in the developed Cobb-Douglas model, the R2-value was higher than that of the quadratic model while RMSE and MAPE of the quadratic model were determined to be smaller than that of the Cobb-Douglas model. Therefore, the best model to investigate the relationship between input costs and the income of wheat production in both mechanized and semi-mechanized systems was the quadratic model.
While the agricultural sector plays a profound role in food provisioning, achieving sustainability poses formidable challenges worldwide. In this context, this study projects the environmental impacts of China's agricultural sector in the years 2030 and 2050 in line with the framework of shared socioeconomic pathways (SSPs) in comparison to the base period from 1994 to 2019. Accordingly, a spatiotemporal assessment was undertaken by employing the Life Cycle Assessment (LCA) method in seven geographical regions across China. In the base period, China's agricultural sector experienced increasing environmental impacts, between ∼10 % to ∼30 % from 1994 to 2014, while it decreased by ∼8 % from 2014 to 2019. On average, 33 % of the total environmental challenges impacted the eastern region of China. The projections also indicate that the transition towards sustainability (SSP1) could reduce environmental impacts by ∼52 % in 2030 and by ∼76 % in 2050, in comparison to 2019. Conversely, adopting regional rivalry strategies (SSP3) can result in a ∼ 3 % increase in environmental impacts in 2030 and a ∼ 30 % decrease in 2050 compared to 2019. The findings underscore the decisive role of agricultural management policies in controlling environmental impacts and facilitating the decarbonization of the Chinese agricultural sector.
Abstract Effective resource allocation in the agri‐food sector is essential in mitigating environmental impacts and moving toward circular food supply chains. The potential of integrating life cycle assessment (LCA) with machine learning has been highlighted in recent studies. This hybrid framework is valuable not only for assessing food supply chains but also for improving them toward a more sustainable system. Yet, an essential step in the optimization process is defining the optimization boundaries, or minimum and maximum quantities for the variables. Usually, the boundaries for optimization variables in these studies are obtained from the minimum and maximum values found through interviews and surveys. A deviation in these ranges can impact the final optimization results. To address this issue, this study applies the Delphi method for identifying variable optimization boundaries. A hybrid environmental assessment framework linking LCA, multilayer perceptron artificial neural network, the Delphi method, and genetic algorithm was used for optimizing the pomegranate production system. The results indicated that the suggested framework holds promise for achieving substantial mitigation in environmental impacts (potential reduction of global warming by 46%) within the explored case study. Inclusion of the Delphi method for variable boundary determination brings novelty to the resource allocation optimization process in the agri‐food sector. Integr Environ Assess Manag 2024;20:1759–1769. © 2024 SETAC
The aim of this study was to determine the probability of working days (PWD) for tillage operation using weather data with Multiple Linear Regression (MLR) and Radial Basis Function (RBF) artificial networks. In both models, seven variables were considered as input parameters, namely minimum, average and maximum temperature, relative humidity, rainfall, wind speed, and evaporation on a daily basis. The PWD was considered to be the output of the developed models. Performance criteria were RMSE, MAPE, and R2. Results showed that the R2-valuewas 0.78 and 0.99 for MLR and RBF models, respectively. Both models had acceptable performance, but the RBF model was more accurate than the MLR model. The RMSE and MAPE values for the RBF model were lower than those for the MLR model. Thus, the RBF model was selected as the suitable model for predicting PWD. Moreover, the results of these models were compared to the prior soil moisture model. It was indicated that the results of the studied models had a good agreement with the results of the soil moisture model. However, the RBF model had the highest R2 (99%). In conclusion, the developed RBF model could be used to predict the probability of working days in terms of agricultural management policies.
A simulation model was developed for secondary tillage and sowing operations in autumn, using discrete event simulation technique in Arena ® simulation software (Version 14).Eight machinery sets were evaluated on a 50-hectare farm.Total costs including fixed-costs, variable costs and timeliness costs were calculated for each machinery set.Timeliness costs were estimated for 21-years period on daily basis (Daily Work method) and compared with another method (Average Work method) based on the equation proposed by ASAE Standards, EP 496.3FEB2006.The Inputs of the model were machinery sets, field size, machines performances and daily soil workability state.The optimization criteria were the lowest costs and lowest standard deviation in daily work method plus the lowest costs based on average work method.The validity of the model was evaluated by comparing the output of the model with field observed data collected from various farms.Results revealed that there was no significant difference (P > 0.01) between the observed and predicted finish day.
Abstract Improving the energy efficiency of fish culture has always been a concern for warm‐water fish farming units. In this respect, the present study explored energy consumption flow and energy indices of warm‐water fish production in Guilan province, northern Iran. The impact of the energy equivalent of the system inputs on the energy equivalent of fish yield was modeled by the Cobb–Douglas function. The results revealed that total energy input, energy ratio, energy productivity, and energy intensity were 3710.4 MJ per 100 m 2 , 0.042, 0.009 kg MJ −1 , and 109.77 MJ kg −1 , respectively. Feed, electricity, and fossil fuel were the most energy‐intensive inputs accounting for 70.09%, 11.95%, and 11.70% of total energy use, respectively; representing the dominant role of feed in the energy input of warm‐water fish farming. Renewable and nonrenewable energy resources accounted for 6.97% and 93.03% of the total energy input of warm‐water fish culture system, respectively; requiring more care to cut the share of nonrenewable energy inputs. The Pareto chart determined that fingerling, labor, and electricity had the highest effects on the warm‐water fish yield. Therefore, more attention should be paid to the appropriate use of these inputs in warm‐water fish culture in the study region.
روز کاری یکی از عوامل تعیین کننده در انتخاب بهینه سیستم ماشین های زراعی بوده و تعیین کننده میزان زمان موجود برای انجام عملیات کشاورزی می باشد. معمولا برای تعیین تعداد روزهای کاری موجود از مدل های شبیه سازی رطوبت خاک استفاده می شود. در این تحقیق نیز، مدل شبیهسازی به منظور محاسبه رطوبت روزانه خاک در مزرعه تهیه شده است. مدل به دست آمده تعداد روزکاری برای انجام عملیات خاک ورزی و کاشت را در پاییز پیش بینی میکند. این مدل تابعی از شرایط آب و هوایی مانند بارندگی، تبخیر و خصوصیات لایه 25 سانتی متری عمق خاک میباشد. برای اعتبارسنجی از دادههای هشت ساله عملیات خاک ورزی ثانویه و کاشت مزرعه ی تحقیقاتی دانشکده کشاورزی دانشگاه فردوسی مشهد استفاده شد. معیار کارپذیری خاک رطوبت کمتر یا مساوی 85 درصد حد ظرفیت زراعی خاک و بارندگی روزانه ی کمتر از 4 میلی متر در نظر گرفته شد. نتایج تحلیل حساسیت مدل نشان داد که تعداد روز کاری با افزایش حد رطوبتی خاک و ضریب زهکشی خاک افزایش می یابد. سازگاری خوبی میان نتایج بهدست آمده از مدل در مقایسه با واقعیت وجود داشت. تعداد روز کاری برای عملیات خاک ورزی ثانویه و کاشت پاییزه در مزرعه با احتمال 50، 80 و 90 درصد بهطور میانگین برای دورههای ده روزه مهر ماه بهترتیب 9/94، 9/21 و 8/57 روز، آبان ماه، 9/77، 8/02 و 6/41 روز و برای آذرماه، 9/68، 7/48 و 5/24 روز به دست آمد.
Exergy analysis is receiving considerable attention as an approach to be applied for making decision toward moving to a sustainable and energy-efficient food supply chain. This study focuses on how selection of variety affects the exergy flow of a crop production system (rice production). In this regard, 9 varieties of rice were investigated in Italy, the largest rice producer in Europe. Sensitivity analysis of inputs consumption and the exergy management scenarios of the most sensitive inputs are also provided in this study. The results indicated that the cumulative exergy consumption value of the investigated rice varieties ranges from 11,682 MJha-1 to 15,541 MJha-1. Chemical fertilizers and diesel fuel consumption were the biggest contributors to the total energy consumption in all investigated varieties. Luna variety, with the cumulative degree of perfection value of 3.87 and renewability indicator of 0.74, was identified as the most exergy efficient variety of rice in Italy.
This study aimed to assess the environmental sustainability of wheat harvesting operation in rainfed and irrigated farming systems in three different locations in Iran, including Sari, Mashhad and Parsabad Moghan counties. Four sustainability indices of energy, emergy, exergy, and greenhouse gas emissions were investigated in this research. Results revealed that the energy efficiency of harvesting operation in irrigated systems was higher than that in rainfed systems. The emergy analysis results highlighted that the environmental sustainability indices for rainfed systems in Mashhad, Parsabad Moghan, and Sari were 0.047, 0.035 and 0.034, respectively. The values for the irrigated systems were 0.036, 0.035 and 0.034, respectively. The results of exergy analysis also indicated that the exergy efficiency of harvesting operation in rainfed and irrigated systems in Sari and Parsabad Moghan was higher than that in other areas by 56.07 and 128.72, respectively. Total GHG emissions of harvesting operation in Sari, Parsabad Moghan, and Mashhad in rainfed systems were determined to be lower than that in the irrigated systems (54.88, 47.64 and 36.03 kg CO2eq ha-1 versus 67.52, 66.56 and 59.22 kg CO2eq ha-1, respectively). In conclusion, the wheat harvesting system was environmentally more sustainable in Sari and Parsabad Moghan counties in rainfed and irrigated farming systems, respectively.
Abstract The purity is accounted for one of the main characteristics of sugar beet juice in the sugar production process. In this regard, in the paper, the impact of slicing parameters including blade type, slicing angle from 0 to 90°, slicing thickness from 3 to 6 mm, and preheating duration from 3 to 15 min was studied on juice purity using Response Surface Methodology (RSM). The Genetic Algorithm (GA) technique was also employed to find the optimum values of variables to reach the highest juice purity. The results indicated that the quadratic model was the best model to predict juice purity. The Findings presented that as cossette thickness and slicing angle increased, the juice purity was improved. Optimization of the quadratic model by GA showed the best cossette thickness was 6 mm for both blades. The results of optimization indicated that 92.25 and 94.45% juice purities could be obtained from optimum conditions.
