Agricultural Production Research Articles

Digitalization and Agricultural Green Total Factor Productivity: Evidence from China

Based on panel data of 31 provinces (autonomous regions and municipalities) in China from 2011 to 2022, this paper empirically examines the impact of digitalization on the inputs and outputs of the agricultural production process, and thereby derives the effects and mechanisms by which digitalization empowers the growth of agricultural green total factor productivity. The study finds that agricultural and rural areas’ digitalization significantly improves agricultural green total factor productivity, and this promotion mainly comes from the improvement of technical efficiency. Further analysis shows that digitalization mainly reduces land input and labor input, increases expected output, and reduces undesired output during the agricultural production process to achieve an improvement in agricultural green total factor productivity, indicating that digitalization has altered the allocation of agricultural factors. Heterogeneity analysis finds that the effect of digitalization on the growth of agricultural green total factor productivity is more pronounced in the eastern regions, southern regions, and areas with higher levels of agricultural digitalization, indicating that the development of digitalization exacerbates the gap in agricultural green total factor productivity among regions.

Reflecting on Democratic and Economic Experiences in sub-Saharan Africa: A Comparative Analysis of the First 3 Decades vs. the Second 3 Decades under Ghana’s Republics

This study sought to juxtapose Ghana’s first three decades [1960s-1980s] of democratic and military regimes with its second three decades [the 1990s-2010s] of only democratic regimes to understand its political and economic trajectories and dynamics. Drawing on the Mandate Theory and leveraging on the interpretivist paradigm, the study adopted a qualitative content analysis and in-depth interviews to collect and analyze the views and opinions of respondents in Accra. The findings show that (a) The 1992 constitution has effectively halted Ghana’s 1960s and 80s cyclical political turmoil, ushering in ‘Pax Ghanaianica’, (b) There is a correlation between campaign promises and real GDP growth under the Fourth Republic, and (c) There is no correlation between a stable democracy and a stable real GDP growth under the Fourth Republic [i.e., The political campaign promises relative to power/electrification/energy have been unable to sustain production in Industry, Agriculture, Commerce, and Services]. The study concludes that Ghana’s roller coaster economic development may re-trigger ‘alien’ incursions into contemporary Ghanaian politics. It recommends that the political economy of Ghana should be structured to propel sustainable production in Industry, Agriculture, Commerce, and Services.

Investigating the effect of changing the K:Ca ratio in nutrient solution on the quantitative and qualitative yield of Lilium spp. in hydroponic cultivation

Nowadays, hydroponics is an advanced method for plant production in agriculture. The optimal balance of nutrients in the nutrient solution is crucial to enhance plant performance in this method. To investigate the effect of potassium to calcium (K:Ca) ratio change in nutrient solution on quantitative and qualitative yield of lilies in hydroponic culture, a randomized complete block design with nine treatments and three replications was designed and carried out in 30% perlite and 70% sand. The treatments included different concentrations of calcium (Ca) at three levels of 0, 3, and 6 mM and different concentrations of potassium (K) at three levels of 0, 3, and 6 mM in the Hoagland nutrient solution, with K:Ca ratios of 0:0, 0:3, 0:6, 3:0, 3:3, 3:6, 6:0, 6:3, and 6:6. Results showed that the presence of different levels of potassium and calcium was found to enhance the quality of lilies by influencing nutrient uptake. The results indicated that nutrient solutions containing different ratios of K:Ca had a significant effect on reproductive height, the number of aborted buds, stem diameter at the 5% level and the number of flowers, the number of leaves, the fresh weight of shoots, the dry weight of shoots, and the longevity at the 1% level. The highest yield in terms of longevity (17 days), the number of flowers, the number of leaves, and absorption of potassium and calcium were observed in the nutrient solution with a 3:3 K:Ca ratio.

Improving sustainability of rice-canola rotation through water and nitrogen management in a humid region

Intensive cropping systems face significant environmental and economic challenges due to current water and nutrient management practices. This study addresses the limited research on optimizing these practices in rice-canola rotations, focusing on enhancing sustainability through improved water and nitrogen (N) management. Utilizing the DSSAT crop growth model, we evaluated three irrigation scenarios for rice- I1 (100 % irrigation requirement: IR), I2 (80 % IR), and I3 (60 % IR)-alongside three N application levels: N1 (30 % below RAN: regional average N), N2 (RAN), and N3 (30 % above RAN). Key sustainability indicators, including water productivity (WP), nitrogen use efficiency (NUE), and economic productivity, were analyzed. The DSSAT model demonstrated strong predictive accuracy, with yield differences of only 236 kg ha⁻¹ for rice and 121 kg ha⁻¹ for canola between observed and simulated values. A 40 % reduction in water use increased rice yield to a maximum of 5654 kg ha⁻¹ under I3N2 conditions. Additionally, this reduction in water consumption resulted in a 27 % decrease in production costs, significantly enhancing WP, net benefits, and gross benefits of rice by 46.7 %, 43.1 %, and 47.9 %, respectively. Conversely, a 30 % increase in N application for canola correlated with a 9 % yield increase, underscoring the importance of tailored N strategies. Furthermore, N3 raised WP, NUE, net benefits, and gross benefits of canola by 6.7 %, 1.4 %, 9.1 %, and 9.2 %, respectively, compared with current N application level. Overall, this study illustrates the potential for substantial water savings in rice cultivation while recommending stable or reduced N application levels. These management strategies enhance farm performance, lower production costs, and contribute to the sustainability of rice-canola cropping systems, promoting both agricultural productivity and environmental stewardship.

Construction of a blockchain based cold chain logistics information platform for Gannan navel oranges to enhance transparency and efficiency

As a high-value agricultural product, the cold chain logistics management of Gannan navel oranges has global significance. Especially in Africa and Europe, food safety and supply chain quality are critical issues. However, the existing Gannan navel orange logistics information management system relies on traditional databases and information systems, with insufficient data sharing and transparency, affecting the overall coordination of the supply chain and customer satisfaction. In addition, the traditional system is vulnerable to hacker attacks and malicious tampering of data by insiders, resulting in serious economic losses and reputation damage. So the blockchain platform constructed in this study improves the transparency and traceability of logistics data through the SMART-PBFT algorithm of the alliance chain, which optimizes the logistics management process, improves the quality of logistics services, and reduces operating costs. It improves the logistics efficiency and data transparency of local agricultural products, and also provides a demonstrative case for the cold chain management of other high-value agricultural products around the world, with broad application potential and reference value.

Soil Erosion Susceptibility Prediction Using GIS-Based Multicriteria Analysis and Worldview-3 Satellite Images

Soil erosion has been identified as a major threat to existing soil deposits, which negatively affects ecosystem sustainability, agricultural production and clean water supply. This study covered the eastern part of Kaštela Bay with the aim of developing a model of susceptibility to soil erosion by applying the GIS-based multicriteria analysis, based on nine determinant criteria categorized by the Jenks method in five classes. The largest representation of soil erosion susceptibility was detected on the slopes of the Kozjak and Mosor massifs. According to the calculated weighting coefficients using the analytic hierarchy process (AHP), the slope was the most impactful criterion with 29.9 % importance. It was followed by hydrological criteria of topographic wetness index, standardized precipitation index and LS-factor, with 13.1 % importance each. After the development of the model, zones of very high and high susceptibility to soil erosion were delineated, with a combined coverage of 33.34 % of the study area. These areas were identified as a potential threat from the future occurrence of negative effects of soil erosion, which should be addressed in the future by land policy managers and government managers.

Evaluation of Thermal Comfort Conditions in the Working Environments of Seasonal Agricultural Workers in Csa Koppen Climate Type

This study focuses on determining the thermal comfort conditions of seasonal agricultural workers during the hot periods of the year when agricultural production is intense in the Aksu/Türkiye region, which is characterized by the Csa climate type according to the Köppen–Geiger climate classification. In this study, the thermal comfort conditions of seasonal agricultural workers working on open farmlands were evaluated in ten-day, monthly, and seasonal periods for 6 months between 5:00 and 21:00 h using the modified Physiological Equivalent Temperature (mPET) index in the Rayman Pro software according to their activity energy during work. The results of the study reveal that increased activity energy leads to a decrease in thermal comfort conditions of agricultural workers, mPET values of agricultural workers engaged in soil cultivation (Group II) are 2.1 to 2.9 °C higher than the mPET values of workers engaged in plant care and harvesting (Group I), and the agricultural workers in Group II are exposed to more heat stress. The thermal comfort conditions of agricultural workers in Group I deteriorate between 09:00 and 16:00 h with mPET values between 34.1 and 35.3 °C and those of agricultural workers in Group II deteriorate between 08:00 and 17:00 h with mPET values between 34.3 and 37.7 °C. In this context, the daily comfortable working time in the morning and afternoon was found to be 9 h for Group I and 7 h for Group II. Overall, determining the comfortable working hours of agricultural workers in regions with different climate types in future studies will be an important resource for decision-makers in developing strategies to protect the health and increase the productivity of agricultural workers.

Promising Directions of Economic Development of Rural Areas: The Case of the Republic of Crimea

Introduction. Rural areas represent significant strategic potential for the socio-economic development of the country. In recent years, the role of the state in planning territorial development has increased significantly. This is due to successful experience in designing territorial and sectoral development in difficult periods of macroeconomic and geopolitical crises, financing and monitoring infrastructure projects that are important for the economy and society. The subject of the study are rural areas localized in the space of one of the constituent entities of the Russian Federation, which recently became part of the state – the Republic of Crimea. The purpose of the article is to substantiate promising directions for the economic development of rural areas of the Republic of Crimea based on the construction of a structural and sectoral profile. Materials and Methods. The research materials were the scientific results of leading scientists studying the problems of the economy of rural areas, structural and sectoral transformations on a regional and municipal levels. The information base of the study was made up of official statistical data of Rosstat and Crimea, official websites of governmental bodies of the Russian Federation, and archival materials. The research was conducted using content analysis of scientific literature, retrospective and structural analysis. Results. Structural and sectoral profiles of the economy of rural areas of the Republic of Crimea for 2017 and 2022 were developed, which allowed to prove the presence of processes of structural and sectoral changes, identify key factors and justify promising directions for further development. The authors prove that against the background of ongoing changes in the economic space of rural areas of Crimea, the dominance of agricultural production based on the use of unique natural factors, resources and conditions (climate, soil fertility, availability of centuries-old technologies for growing agricultural products, etc) remains. Discussion and Conclusion. The decrease in the share of agricultural production in the sectoral structure of the economy of rural territories (municipalities) of the Republic of Crimea is the most significant for the period 2017–2022, which is primarily due to the following factors: strengthening of transport, logistics and recreational and resort functions of the region, development of other types of material production. Analysis of the structural and sectoral profile proves the sustainability of the development of rural municipalities of the Republic of Crimea, due to the focus on the needs of both visitors and local population. It is advisable to use the research results in the development of strategies, plans, projects for the socio-economic development of rural territories, municipalities and regions.

Supply Chain Efficiency and Agricultural Product Sales: A Comparative Study of 2022 and 2023 Trends in USA

This comparative study analyzes supply chain efficiency and agricultural product sales trends in the USA between 2022 and 2023. Utilizing a comprehensive dataset of agricultural products—including livestock, fruits, and dairy—the study examines key metrics such as units shipped, units sold, and units on hand across various product categories. By evaluating these variables, the research highlights changes in supply chain performance and market dynamics over the two-year period. The study focuses on identifying shifts in product demand, sales performance, and supply chain efficiency, offering insights into how agricultural producers adapted to market conditions. Results indicate that while some categories, such as fruits, experienced consistent sales growth, others faced fluctuations due to supply chain disruptions and changing consumer preferences. This analysis not only underscores the importance of efficient supply chains but also opens avenues for predictive analysis, providing actionable recommendations for improving supply chain practices in the agricultural sector. The findings are particularly relevant for industry practitioners aiming to enhance supply chain resilience and optimize product sales performance.

«Умная деревня» как перспектива развития сельских территорий

The article substantiates the role and importance of rural areas in the country's economy. Data on the dynamics of the number of both urban and rural populations are presented. The importance of implementing the concept of introducing "Smart villages" as one of the ways to solve the problem of rural depopulation is considered. This is relevant both from the point of view of the need to modernize and increase the competitiveness of agricultural production, and taking into account the geographical features of territories and different population densities in different regions of the country. The main interpretations of the concept of "smart village" using foreign sources are highlighted, as well as the basic principles of the concept of "smart village" are highlighted. As an example of the implementation of the concept, the domestic and foreign experience of the functioning of "smart villages" is given. The main technologies introduced in the implementation of the "smart village" concept are considered and organizational and managerial recommendations are proposed for the further effective development of "smart" rural areas.

Tropical forest cover, oil palm plantations, and precipitation drive flooding events in Aceh, Indonesia, and hit the poorest people hardest.

Tropical forest loss and degradation in watersheds disrupt essential ecosystem services that regulate water flow, often causing devastating floods that impact agricultural productivity and impoverish downstream communities. Despite its importance, evaluations of the interconnectedness between the depletion of hydrological services and flooding lack an evidence-base in the Global South and, therefore, have little influence on policy dialogue. In this study, we focus on the forest-rich province of Aceh, Indonesia, using local and national newspaper articles to compile information on flood events between 2011 and 2018. We explored spatio-temporal flood patterns with a combination of climatic, topographic, and environmental factors. We compiled 2,029 reported flood events in mainland Aceh located in 20 of the 21 districts/cities, with a disproportionately high occurrence (71%) in four districts. The trend of flood events exhibited an increasing pattern between 2011 and 2018. Over this period, floods displaced ~158,000 people and damaged ~24,500 houses and ~11,500 ha of agricultural land. Our generalized linear mixed-effect model found that reported flood events were more likely to occur in areas with lower tree cover, more oil palm plantations, and higher precipitation. Areas with a lower human population density and higher poverty rates were found to be most susceptible to flooding events. Our findings highlight the critical link between forest preservation and flood prevention, and the irreplaceable role that forests play in ensuring the well-being of local communities, especially those affected by poverty. Our study underscores the importance of considering these interconnected factors in future land use and economic development plans and policies.

Effects of winter soil warming on crop biomass carbon loss from organic matter degradation

Global warming poses an unprecedented threat to agroecosystems. Although temperature increases are more pronounced during winter than in other seasons, the impact of winter warming on crop biomass carbon has not been elucidated. Here we integrate global observational data with a decade-long field experiment to uncover a significant negative correlation between winter soil temperature and crop biomass carbon. For every degree Celsius increase in winter soil temperature, straw and grain biomass carbon decreased by 6.6 ( ± 1.7) g kg-1 and 10.2 ( ± 2.3) g kg-1, respectively. This decline is primarily attributed to the loss of soil organic matter and micronutrients induced by warming. Ignoring the adverse effects of winter warming on crop biomass carbon could result in an overestimation of total food production by 4% to 19% under future warming scenarios. Our research highlights the critical need to incorporate winter warming into agricultural productivity models for more effective climate adaptation strategies.

A Theoretical Perspective for Ammonia Synthesis: Nitric Oxide or Nitrate Electroreduction?

Ammonia is an important raw material for agricultural production, playing a key role in global food production. However, conventional ammonia synthesis resulted in extensive greenhouse gas emissions and huge energy consumption. Recently, researchers have proposed electrocatalytic reverse artificial nitrogen cycle (eRANC) routes to circumvent these issues, which can be driven by electrocatalysis and sustainable electricity. Here, a theoretical and computational perspective on the challenges and opportunities with the comparison with experimental results: electrochemical reduction of nitrate (eNO3RR) and nitrite (eNO2RR), electrochemical reduction of nitric oxide (eNORR) combined with oxidative nitrogen fixation are presented. By comparison, the N2→NO→NH3 route is proposed as the most promising in case the NO solubility can be solved well in reactor design. Its high efficiency of ammonia production is demonstrated. Instead, the eNO3RR can be another choice because it is non-toxic and the solid-liquid interface is usually efficient for electrochemical reactions, while its low selectivity at low overpotentials is an issue. These fundamentals highlight the potential and key factors of eRANC as an efficient and sustainable route for ammonia production.

Quantifying Soil Loss in the Brazilian Savanna Ecosystem: Current Rates and Anticipated Impact of Climate Changes

ABSTRACTThe Brazilian Savanna (Cerrado) is the second‐largest South American biome that corresponds to almost two‐third of the national agricultural production. Extensive agricultural‐driven land‐use changes have significantly altered the landscape, causing increased soil erosion. Furthermore, projections of climate change effects on the Cerrado raise concerns about the potential exacerbation of soil loss and its consequences on ecosystem sustainability. This study investigated soil loss for the Cerrado ecosystem by assessing current rates and projecting the potential effects of future climate change. Current soil loss was based on experimental plots (100 m2) during 7 years maintained under typical main land cover in Brazil (sugarcane, pasture, Cerrado, and bare soil). Erosivity, by using the Universal Soil Loss Equation (USLE), was estimated from observations, parameters of erodibility, and land cover. To assess the future soil loss (2100), we used the calibrated USLE equation with yearly erosivity derived from 12 downscaled and bias‐corrected SSP2‐4.5 and SSP5‐8.5 scenarios of CMIP6 climate model projections. Current agricultural practices induce considerable erosion, where sugarcane has 3.4 times higher soil loss as compared with the natural soil cover. Regarding future SSP2‐4.5 and SSP5‐8.5 scenarios (2100), we estimated an increase of 4.9% and 7.6% in soil loss, respectively, for all land covers. The observed soil loss rates underscore the critical importance of implementing sustainable land management practices to mitigate further soil degradation. Climate change may impose additional stress on the Cerrado ecosystem, amplifying the urgency for adaptive measures to safeguard this important biome.

Intelligent Vision System with Pruning and Web Interface for Real-Time Defect Detection on African Plum Surfaces

Agriculture stands as the cornerstone of Africa’s economy, supporting over 60% of the continent’s labor force. Despite its significance, the quality assessment of agricultural products remains a challenging task, particularly at a large scale, consuming valuable time and resources. The African plum is an agricultural fruit that is widely consumed across West and Central Africa but remains underrepresented in AI research. In this paper, we collected a dataset of 2892 African plum samples from fields in Cameroon representing the first dataset of its kind for training AI models. The dataset contains images of plums annotated with quality grades. We then trained and evaluated various state-of-the-art object detection and image classification models, including YOLOv5, YOLOv8, YOLOv9, Fast R-CNN, Mask R-CNN, VGG-16, DenseNet-121, MobileNet, and ResNet, on this African plum dataset. Our experimentation resulted in mean average precision scores ranging from 88.2% to 89.9% and accuracies between 86% and 91% for the object detection models and the classification models, respectively. We then performed model pruning to reduce model sizes while preserving performance, achieving up to 93.6% mean average precision and 99.09% accuracy after pruning YOLOv5, YOLOv8 and ResNet by 10–30%. We deployed the high-performing YOLOv8 system in a web application, offering an accessible AI-based quality assessment tool tailored for African plums. To the best of our knowledge, this represents the first such solution for assessing this underrepresented fruit, empowering farmers with efficient tools. Our approach integrates agriculture and AI to fill a key gap.

Classification of Garlic Varieties with Fluorescent Spectroscopy Using Machine Learning

Machine learning techniques can produce fast, accurate and objective results in the analysis of agricultural products. These artificial intelligence-based systems are frequently encountered in studies on agriculture in the literature. This study reveals the usability of machine learning algorithms in classification of garlic cultivars using fluorescent spectroscopic data. For this, six types of garlic were used: Razgradski-11, Razgradski-12, Razgradski-115, Plovdivski-120, Yambolski-99 and Topolovgradski. In the first stage, the parsing analysis made from the fluorescent spectroscopic data of the garlics was carried out with seven different machine learning. The classification results of these seven types of machine learning algorithms were obtained. In the second stage, the classification results were obtained by adjusting the hyperparameters of each Machine Learning (ML) algorithm in order to control the improvability of the classification accuracy rates. Finally, performance metrics such as Specificity, precision, MCC, F1-Score of the classification processes obtained in the two stages were compared. In general, it was observed that the classification performances increased with the hyperparameter adjustment performed in the second stage. In this study, classification results with ML showed that fluorescent spectroscopy data of garlic strongly represented garlic species and provided high performance classification accuracy of 99.93% with Neural Network (NN), one of the machine learning methods using these data.

Тенденции развития потребительских предпочтений на рынке мяса и мясных продуктов

The article examines the trends in the development of consumer preferences for meat and meat products. During the analysis of the structure of the food market, consumer surveys and research on production technology and consumption statistics, the main patterns of changing preferences of domestic consumers were identified. The data on the dynamics, structure, volumes of production and consumption of meat and meat products are presented. The data on the volumes of meat and meat products consumption recommended by the Ministry of Health of the Russian Federation and their actual consumption are presented. Statistical data illustrating the dynamics of the volume of state support for agricultural producers are presented.

Economic and Environmental Effects of Farmers’ Green Production Behaviors: Evidence from Major Rice-Producing Areas in Jiangxi Province, China

This study examines the economic and environmental impacts of green production practices among farmers. It aims to contribute to sustainable agricultural development, mitigate agricultural non-point source (NPS) pollution, and align environmental protection with economic growth. This paper utilizes survey data from 1345 farm households in the main rice production areas of Jiangxi Province, China, using the example of reduced fertilizer application (RFA) among rice farmers. This study constructs a slack-based measure data envelopment analysis (DEA—SBM) model with undesirable outputs to measure environmental effects and applies an endogenous switching regression model (ESRM) to test the economic and environmental effects of farmers’ adoption of green production technologies. We found the following: (1) The RFA behavior of farmers has a significant positive impact on their net profit per hectare (NPH), helping farmers increase their income, with the increase ranging from 2.05% to 6.54%. (2) Farmers’ RFA behavior has a significant positive impact on agricultural green productivity (AGP), contributing to the improvement of the environment, ranging from 44.09% to 45.35%. (3) A heterogeneity analysis found inconsistencies in the income-enhancing and environmental-enhancing effects at different quantiles of NPH and AGP. Therefore, attention should be placed on improving the agricultural product quality supervision system under the market circulation mechanism, creating land scale conditions conducive to the promotion and application of fertilizer reduction technologies and promoting the implementation of externality internalization compensation systems.

EU food price inflation amid global market turbulences during the COVID‐19 pandemic and the Russia–Ukraine War

AbstractSince the COVID‐19 pandemic and the Russia–Ukraine War, global food markets have been in turmoil. Agricultural input and energy prices doubled between 2020 and 2022, with immediate consequences on food accessibility. We examine the drivers of the EU food inflation patterns, and how trade integration shapes these dynamics. We find that food price inflation has been mainly driven by surges in agricultural production costs and, to a lesser extent, by global food price increases. Trade openness has not exacerbated the inflating dynamics during this period. We conclude with reflections on inflationary, production, and trade policies.

Performance of artificial intelligence model (LSTM model) for estimating and predicting water quality index for irrigation purposes in order to improve agricultural production.

The primary goal of this study is to predict the current and future water quality index for irrigation (WQII) of the western Mitidja alluvial aquifer in northern Algeria. The modified WQII was used to evaluate groundwater suitability for irrigation through geographic information system (GIS) techniques. Additionally, a long short-term memory (LSTM) model was employed to calculate the WQII and map future groundwater quality, considering factors like overexploitation, anthropogenic pollution, and climate change. Two scenarios were analyzed for the year 2030. Results from applying the modified WQII model to 2020 data showed that about 83% of the study area has medium to high groundwater suitability for irrigation. The LSTM model exhibited strong predictive accuracy with determination coefficients (R2) of 0.992 and 0.987, and root mean square error (RMSE) values of 0.061 and 0.084 for the training and testing phases, respectively. For the first 2030 scenario, the area with low and medium groundwater suitability is expected to increase by 4% and 7% compared to the 2020 map. Conversely, under the second scenario, groundwater quality is predicted to improve, with a decrease of 14% and 11% in the low and medium suitability areas. The combination of the modified WQII and LSTM model proves to be an effective tool for estimating and predicting water quality indices in similar regions globally, offering valuable insights for water resource management and decision-making processes.