Original Article
Mansoureh Mozaffari Gonbari; Bahareh Jamshidi; Jaber Soleymani; Parisa zargaripour
Abstract
Monitoring potato quality attributes during storage is essential for optimizing supply chain management, ensuring timely product distribution, and minimizing postharvest losses. In this study, a non-destructive monitoring system for potato quality assessment was developed and evaluated using laser light ...
Read More
Monitoring potato quality attributes during storage is essential for optimizing supply chain management, ensuring timely product distribution, and minimizing postharvest losses. In this study, a non-destructive monitoring system for potato quality assessment was developed and evaluated using laser light backscattering imaging in the visible and near-infrared spectral regions. Backscattering images were acquired throughout the storage period under three storage conditions: cold storage at 4°C, cold storage at 7°C, and traditional storage. Two potato cultivars, Agria and Jelly, were included in the experiments. Key quality attributes, including moisture content, starch content, total soluble solids (TSS), and texture firmness, were measured periodically during storage. Features based on intensity and texture analyses of images were extracted. An artificial neural network model was employed to establish the relationship between the mentioned properties and image features. The results indicated that the 680 nm wavelength was the most effective for predicting moisture content, whereas the 880 nm wavelength provided superior performance for predicting firmness. The combination of both wavelengths performed well in predicting starch content and total soluble solid content. The highest correlation coefficients obtained for the prediction of moisture content, total soluble solids, firmness, and starch content were 0.76, 0.82, 0.71, and 0.73, respectively. These findings demonstrate the possibility of using laser backscattering imaging system for assessing the attributes of stored potatoes.
Original Article
Mohammad Nasiri; Mohammad Gahderijani; Babak Beheshti
Abstract
The technical development of rice mechanization in the northern provinces of Iran has accelerated in recent years, encompassing an expansion in mechanization levels, the increased adoption of transplanting and harvesting machinery, and the ongoing renewal of the agricultural machinery fleet. However, ...
Read More
The technical development of rice mechanization in the northern provinces of Iran has accelerated in recent years, encompassing an expansion in mechanization levels, the increased adoption of transplanting and harvesting machinery, and the ongoing renewal of the agricultural machinery fleet. However, the precise impact of these advancements on energy demand, particularly under varying technical and managerial conditions, remains insufficiently explored. This study investigates the influence of technical mechanization development on energy demand, while also examining the moderating roles of energy efficiency, energy carrier prices, and fleet technology levels. Data spanning from 2013 to 2023 were collected from the three primary rice-producing provinces: Guilan, Mazandaran, and Golestan. For energy indicator calculations, locally-adapted energy equivalent coefficients specific to Iran's conditions were employed, derived from validated domestic studies (Alam et al., 2023; Zahedi et al., 2015). Energy efficiency was assessed using the DEA-BCC model, and the dynamic relationships were estimated through a System-GMM panel data approach. The findings reveal that the average pure technical efficiency was highest in Golestan (0.92), followed by Mazandaran (0.85) and Guilan (0.81). Moreover, the highest and lowest total energy inputs were recorded in Golestan (77,685 MJ ha⁻¹) and Guilan (34,188 MJ ha⁻¹), respectively. The System-GMM estimates indicate a non-linear, U-shaped relationship between the mechanization level and energy intensity. In Golestan, increasing mechanization up to a threshold of 3.6 hp ha⁻¹ led to a reduction in energy intensity, whereas in Guilan, the effect was non-uniform: decreasing at lower levels but increasing at higher levels due to the lower adoption of modern technologies and greater land fragmentation (P < 0.05). The price elasticity of energy demand was estimated to be higher in Guilan (0.13) than in Golestan (0.08). Among the factors examined, energy efficiency emerged as the most significant contributor to reducing energy intensity across all three provinces. The results underscore that the effect of technical mechanization development on energy demand is contingent upon regional conditions, fleet technology levels, operational structures, and managerial efficiency. Consequently, rice mechanization development policies should be tailored to the specific technical and structural characteristics of each province.
Original Article
Adel Vahedi
Abstract
Energy is used in different ways in the dairy industry and is wasted in the milk production process for various reasons. By using different production methods, livestock farmers play an essential role in the energy efficiency of their production unit. This research was carried out in 2024 in Tehran province ...
Read More
Energy is used in different ways in the dairy industry and is wasted in the milk production process for various reasons. By using different production methods, livestock farmers play an essential role in the energy efficiency of their production unit. This research was carried out in 2024 in Tehran province to find out the indices of energy consumption, identifying efficient and inefficient dairy cattle producing farms in order to increase the efficiency of production units to the maximum and calculating the amount of energy that can be saved resulting from the optimization of energy consumption. Information was collected through questionnaires and document studies and random sampling from different dairy cattle units, and all the activities performed throughout the year were recorded. The input energy includes labor, machinery and equipment, fuel, electricity, livestock feed (hay, silage corn, concentrate and wheat straw), buildings and facilities, and the output energy includes milk, meat and cow manure produced. Energy indices including net energy gain, energy use efficiency, energy productivity and specific energy were determined. With the approach of Data Envelopment Analysis(DEA), the energy efficiency of the dairy farms was determined and the amount of energy consumption optimization in inefficient units and the total energy that could be saved was determined. The results showed that the total amount of input energy is 193690.75 MJ per head of cow, and animal feed had the largest share of input energy with 76.96%. Among the main feeds consumed by dairy cows, concentrate, alfalfa, corn and straw contributed 32.99%, 27.68%, 13.8% and 2.48% of the total energy consumption, respectively. The amount of total energy output was 36886.482 MJ per head of cow, and milk production had the largest share with 87.02% of the total energy output. The energy use efficiency, net energy gain, energy productivity and energy intensity were determined as 0.19, -156804.272 MJ per a head of cow, 0.086 kg/MJ and 11.636 MJ/Kg respectively. Average technical efficiency, pure technical efficiency and scale efficiency were calculated for dairy farms in Tehran province as 0.892, 0.931 and 0.958 respectively. The average target energy in dairy farms of Tehran province was determined to be 129, 764.15 MJ per a head of cow. The conducted research showed that by optimizing energy consumption in inefficient dairy farms in Tehran province, an average of 63926.53 MJ per a head of cow can be saved and energy use efficiency and energy productivity can be upgraded 0.284 and 0.128 Kg/MJ, respectively.
