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Agriculture, one of the most important sectors of the Iranian economy that plays a vital role in providing food security and job opportunities, has always been faced with a lack of financial and credit resources. Therefore, identifying the drivers of credit supply to this sector is of great importance. The main objective of this study was to determine the factors affecting Agriculture Bank (Bank Keshavarzi of Iran) credit supply as the main source for financing agricultural activities, in Iran. In this regard, provincial panel data for period 2007-2020 and non-linear autoregressive distributed lag model, which distinguishes this research from those of previous years, have been used. The results indicate the asymmetric effect of all independent variables on credit supply of Agricultural Bank or the superiority of non-linear model in explaining the relationship between variables. For example, the positive shock on the value of bank assets with coefficient of 0.18 and its negative shock with coefficient of -0.05 will affect the growth of credit supply in the long run. Based on research findings and in order to increase credit supply, it is recommended that Agricultural Bank put the control of non-current receivable more effectively (especially through careful evaluation of borrowers' eligibility) in its policy priorities and, therefore, reduce credit risk and perform more effective services in financing of agricultural sector. In addition, an increase in the bank's assets through investment in modern information and communication technologies is strongly recommended.

This research introduces a deep learning system for the early identification and categorization of wheat illnesses, with the objective of optimizing crop health and promoting agricultural sustainability. Results in up to high classification accuracy for brown rust, yellow rust, leaf rust, and septoria. The combination of artificial intelligence (AI) with image processing methodologies such as rescaling and augmentation allows the system to accurately classify wheat crops that are well or unhealthy. The presented system is of great interest for precision agriculture, providing an affordable means to reduce the application of pesticides and encourage sustainable agricultural practices. Ongoing research involves linking this diagnostic platform with drone technology to facilitate on-demand, point-by-point disease surveillance and monitoring across large areas, further extending the platform’s applicability in field applications for food securit.

This work addresses pattern recognition in the agronomic domain, with a particular emphasis on identifying plant leaves using an adaptive neural network technique. We introduce a tool designed for two primary groups: botany researchers and a broader range of scientists applying it to plant identification and classification. We delve into the capabilities of Deep Learning, focusing on generalization abilities that enable accurate predictions on unseen data, which is essential for handling the variation in leaf shapes, sizes, and structures across species. The implementation details of these neural networks are described, including data preprocessing, network architecture design, training strategies, and evaluation techniques to ensure robustness and reliability in real-world applications.

Precision Agriculture (PA) application aims to increase crop productivity while minimizing environmental impacts. We analyzed the topics most studied in the advancement of crop production in Brazil by applying the concepts of PA using the systematic literature review (SLR). A multi-method approach combined an SLR applying the PRISMA method and secondary data analysis. We found five clusters of technologies using the PA concept related to hardware development and four clusters related to applying technologies to software development in the PA concept. Most topics focused on using sensors to control water (soil and environment), soil electrical conductivity, and data communication. The focus on sustainability led researchers to reduce chemical products related to fertilizers and pesticides using Variable Rate Fertilizers (VRT) and reducing the environmental loading. According to the research results, it was evident that PA technology might help farmers make more accurate decisions about cultivation, production, harvest, and soil management. The availability of decision support systems powered by big data and artificial intelligence to select the best crop for a given season and soil might assist Brazil's sustainable growth of food production.

Meat as one of the most important resources of protein has a special role in human nutrition. Understanding the meat consumption structure of households is essential for planning and policymaking in this regard. In this research, we studied consumption patterns of meat products including chicken, veal, lamb, and fish for households in Iran (Mashhad city) using demand system estimation. The hypothesis of this study is that chicken is a necessary goods and other types of meat are luxury goods. Given the cross-sectional nature of the data and presence of zero expenditure for some households, we used the censored demand model based on a consistent two-step approach. For this purpose, at first, four Probit models were estimated to determine the factors affecting the probability of purchasing each selected meat product. After that, the probability density function (PDF) and the cumulative distribution function (CDF) were calculated for each selected meat product, and the Almost Ideal Demand System (AIDS) considering PDF and CDF was estimated for all types of meat using a non-linear seemingly unrelated regression. Also, the effect of demographic variables on meat consumption pattern was considered in demand system. The results of expenditure elasticities confirmed the hypothesis. The highest own-price elasticity was related to veal. Based on compensated price elasticities, all types of meat were net substitutes for chicken and chicken was also a net complement for all types of meat. On the other hand, the only substitute for lamb and chicken was veal, but with compensating income effect fish also became a substitute for them. So, in the event of an increase of the price of lamb and chicken, we recommend subsidizing the consumers with low purchasing power in order to increase the diversity of consumption of protein products. This can increase the consumption of fish.

Despite the economic advantages of introducing new agricultural technologies into the production system, their rate of adoption in Morocco remains relatively low. The objective of this article is to study the factors that hinder the adoption of these new technologies. We address the case of the recommended fertilizer doses (RFD). The study employs a probit model with a stratified random sampling approach.The data were collected from 297 farmers in the Casablanca-Settat region using a face-to-face interview method and analyzed through R software. The results of the study show that the main barriers are related to access to information and bank credit, government incentive, production orientation, distance to the market as well as age, and level of education.

The study provides a new perspective for the entire investigation, providing an overview of the theoretical implications related to behavior, participation intent, and the digitization of e-agriculture on e-commerce platforms. In addition, the main point is the approach of durian farmers specifically in Tien Giang province, about the intention to participate in e-agriculture. This study presents relevant factors that have an impact on the intention to participate in e-agriculture, specifically durian farmers. From the actual situation of agriculture in the Mekong Delta, typically Tien Giang province. Especially the theoretical of agriculture, e-agriculture and the intention to participate in e-agriculture on the e-commerce platform, an empirical approach in Tien Giang province, Vietnam. The model proposes factors including Social Impact for Agriculture, Adaptive Capacity, Agriculture Awareness, Digitalizations of e-agriculture, e-agriculture on e-commerce has a positive impact on the intention to participate in e-agriculture on e-commerce platforms. This research data was surveyed by direct interviews with 210 durian farmers in Cai Be district of Tien Giang province. Research methods using PLS-SEM. The results of the study show that the factors that have an impact on the intention to participate in e-agriculture on the e-commerce platform: The case of durian farmers. From the fact that this study serves as a premise and proposes implications that are appropriate for further studies on the intention to participate in e-agriculture on the e-commerce platform, related to the intention to participate (e-agriculture) of farmers, especially durian fruit e-agricultural products. An experimental evidence in Tien Giang province, Vietnam.

This paper estimates the shadow values of total carbon sequestration in Czech cereal production. We use a production model with multiple outputs and inputs, using an input distance function (IDF) to estimate shadow price of land. The shadow prices of land and the amount of total carbon sequestration are then used to estimate the shadow values of carbon sequestration for a selected group of crops. The results present considerable differences in shadow values across both crops types and farm sizes.