Search Results (6,792)

Accurate banana yield prediction is essential for optimizing agricultural management and ensuring food security in tropical regions, yet traditional estimation methods remain labor-intensive and error prone. This study developed a predictive model for banana yield in Buena Fé, Ecuador, using Random Forest integrated with phenological data, soil properties, spectral technology, and UAV imagery. Data were collected from a 75.2 ha banana farm divided into 26 lots, combining multispectral drone imagery, soil physicochemical analyses, and banana agronomic measurements (height, diameter, bunch weight). A rigorous variable selection process identified six key predictors: NDVI, plant height, plant diameter, soil nitrogen, porosity, and slope. Three machine learning algorithms were compared through 5-fold cross-validation with systematic hyperparameter optimization. Random Forest demonstrated superior performance, with R² = 0.956 and RMSE=1164.9 kg ha⁻¹, representing only CV = 2.79% of mean production. NDVI emerged as the most influential predictor (importance = 0.212), followed by slope (0.184) and plant structural variables. Local sensitivity analysis revealed distinct response patterns between low- and high-production scenarios, with plant diameter showing the greatest impact (+74.9 boxes ha⁻¹) under limiting conditions, while NDVI dominated (−140.4 boxes ha⁻¹) under optimal conditions. The model provides a robust tool for precision agriculture applications in tropical banana production systems. Full article

The valorization of agricultural by-products through their integration into biodegradable materials represents a promising approach for sustainable food preservation. In this study, nanostructured chitosan/polyvinyl alcohol (PVA)/orange peel–bagasse waste (OPB) (0.125%, 0.25%, and 0.5% OPB) films were developed and characterized for their physicochemical, mechanical, and biodegradation properties. Scanning electron microscopy and confocal laser scanning microscopy revealed that OPB concentration influenced structural homogeneity. Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) revealed possible molecular interactions among components through hydrogen bonding (peaks at 1570, 1416 cm⁻¹, and 1020 cm⁻¹) and imine (C = N) formation (broadening of the peak at 1425 cm⁻¹). As OPB increased, water vapor diffusion and film rigidity increased, while elongation at break decreased. After composting, weight loss was 93.7% and 100% for the chitosan and PVA films, respectively. For the nanostructured films, weight loss was between 94.7% (30PVA/0.5OPB) and 99.7% (30PVA/0.125OPB). Regarding ATR-FTIR of the blends, the intensity of the peaks located between 3625 and 3005 cm⁻¹, at 2919 cm⁻¹, at 1729 cm⁻¹, at 1621 cm⁻¹, at 1521 cm⁻¹, and between 1160 and 885 cm⁻¹, corresponding to the OPB functional groups, decreased. These results demonstrate that incorporating citrus waste enhances biodegradability and provides films barrier properties suitable for fresh produce preservation. Full article

Digital tools and platforms offer significant potential to address critical gaps in market access, credit availability, and agricultural knowledge, particularly in urban and peri-urban areas. This is especially relevant in regions like Sudan, where these opportunities remain largely underexplored. By providing real-time market information, facilitating financial access, and offering essential agricultural training, these tools can help bridge traditional barriers, improve decision-making capabilities, and contribute to sustainable agriculture. Such advancements strengthen economic resilience and promote equity in agriculture, enabling these farmers to drive innovation and sustainability in the industry. Our study was conducted in Omdurman’s Algamwai area during 2022 and 2023, and involved interviews with 100 female farmers. It explored the intersection of gender, technology, and socioeconomic equity. It highlighted how technological advancements can enhance agricultural productivity and market access while addressing challenges such as limited digital literacy and socioeconomic constraints. Despite structural inequalities—including restricted land ownership (45%), limited credit access (5%), and inadequate extension services—female farmers are driving innovation and sustainability by adopting sustainable practices, enhancing food security, and building community resilience. Digital urban agriculture provides income opportunities (76% rely on it) and serves as a platform for equitable participation. From a social science perspective, this research underscores the need to address systemic disparities to unlock the full potential of farmers. Policies ensuring equitable access to resources, credit, and technology are essential for fostering participation and maximizing the socio-economic benefits of digital agriculture in Sudan and similar contexts. Full article

At the European level, potato cultivation is highly polarized. In Western Europe (Germany, France, the Netherlands, Belgium, Denmark), yields are high, agricultural technology is advanced, and production systems ensure stability and competitiveness. In contrast, in Eastern and Southern Europe (including Romania, Poland, Italy, and Spain), yields are considerably lower due to the use of outdated agricultural practices, a low degree of mechanization, and increased exposure to adverse climatic factors. In Romania, potato cultivation is marked by significant territorial disparities and structural imbalances, influenced by land fragmentation, agro-pedoclimatic variability, and the lack of capital necessary for investments in modern technologies and irrigation systems. This study analyzes these regional disparities in relation to the country’s real agricultural potential and quantifies the economic impact of its failure to realize it. The methodology applied is based on descriptive statistical analysis of data at the county and regional level for the period 2003–2024, including minimum, maximum, average, and standard deviations of yields. These were integrated into a production function that correlates cultivated areas with average prices, highlighting major intra-regional differences and significant economic consequences at the national level. The results indicate a double crisis: a drastic reduction in the areas cultivated with potatoes (from 196,000 ha in 2017 to 76,000 ha in 2024) and consistently low yields (12,000–18,000 kg/ha), which led to the collapse of total production (from 3.1 million tons in 2017 to under 1 million tons in 2024). As a result, Romania registers a productivity three to four times lower than the reference Western European countries. Moreover, Romania has moved from being a net exporter to a net importer of potatoes, with the food self-sufficiency indicator decreasing from 100.3% in 2017 to 48.1% in 2023. Although domestic production could theoretically cover consumption needs, structural problems regarding yields, the sharp reduction in cultivated areas, and distribution deficiencies have seriously affected the balance of the domestic market. While per capita consumption has remained relatively constant, the decline in production has led, after 2021, to an increasing dependence on imports. These trends highlight the need for urgent structural reforms, technological modernization, and targeted agricultural policies to increase productivity and restore food security in the Romanian potato crop system. Full article

Early prediction of common bean (Phaseolus vulgaris L.) yield is essential for improving productivity in tropical agricultural systems. In this study, we integrated canopy structural metrics obtained with the Tracing Radiation and Architecture of Canopies (TRAC) system, unmanned aerial vehicle (UAV)-based multispectral measurements (normalized difference vegetation index—NDVI, projected canopy area), and phenological variables collected from stages R6 to R8 under non-limiting nitrogen conditions. Exploratory analyses (correlation, variance inflation factors—VIF), dimensionality reduction (principal component analysis—PCA), and regularized regression (Elastic Net/LASSO), combined with bootstrap stability selection, were applied to identify a parsimonious subset of robust predictors. The final model, composed of six variables, explained approximately 72% of the variability in plant-level grain yield, with acceptable errors (RMSE ≈ 10.67 g; MAE ≈ 7.91 g). The results demonstrate that combining early vigor, radiation interception, and canopy architecture provides complementary information beyond simple spectral indices. This non-destructive framework delivers an efficient model for early yield estimation and supports site-specific management decisions in common bean with high spatial resolution. By enhancing input-use efficiency and reducing waste, this approach contributes to sustainable development and aligns with the global Sustainable Development Goals (SDGs) for climate-resilient agriculture. Full article

Background: The growing global demand for grains and the pursuit of greater efficiency in agroindustrial production processes have fueled scientific interest in technologies for automatic visual inspection of agricultural grains (AVIAG). Despite the increasing number of studies on this topic, few have addressed the practical implementation of these technologies within industrial environments. Objective: This study aims to investigate the technological demands, analyze the potential applications, and identify the challenges for technology transfer of AVIAG technologies to the agroindustrial sector. Methods: The methodological approach combined a comprehensive literature review, which enabled the mapping of AVIAG technology applications and technological maturity levels, with a structured survey designed to identify practical demands, challenges, and barriers to technology transfer in the agricultural sector. Results: The results show that most of the proposed solutions exhibit low technological maturity and require significant adaptation for practical application, which undermines the discussion on technology transfer. Conclusions: The main barriers to large-scale adoption of AVIAG technologies include limited dissemination of scientific knowledge, a shortage of skilled labor, high implementation costs, and resistance to changes in production processes. Nonetheless, the literature highlights benefits, such as increased automation, enhanced operational efficiency, and reduced post-harvest losses, which reinforce the potential of AVIAG technologies in advancing the modernization of the agroindustrial sector. Full article

The soil microbiome is an essential component of agroecosystems. However, managing it remains a challenge due to our limited knowledge of how various environmental factors interact and shape its spatial distribution. This study presents a hierarchical ecological model to explain the assembly of the microbiome in sloping agricultural landscapes. Through a comprehensive analysis of bacterial and fungal communities, as well as the examination of metabolic and phytopathogenic profiles across a topographic gradient, we have demonstrated that topography acts as the main filter, structuring bacterial communities. Land use, on the other hand, serves as a secondary filter, refining fungal functional guilds. Our results suggest that hydrological conditions in floodplains favor the growth of stress-tolerant bacterial communities with low diversity, dominated by Actinomycetota. Fungal communities, on the other hand, are directly influenced by land use. Long-term fallow periods lead to an enrichment of arbuscular mycorrhiza, while agroecosystems shift towards pathogenic and saprotrophic niches. Furthermore, we identify specific topographic positions that may be hotspots for phytopathogenic pressure. These hotspots are linked to certain taxa, such as Ustilaginaceae and Didymellaceae, which may pose a threat to plant health. The derived hierarchical model provides a scientific foundation for topography-aware precision agriculture. It promotes stratified management, prioritizing erosion control and soil restoration on slopes, customizing nutrient inputs in fertile floodplains, and implementing targeted phytosanitary monitoring in identified risk areas. Our research thus offers a practical framework for harnessing soil spatial variability to improve soil health and proactively manage disease risks in agricultural systems. Full article

The three northeastern provinces of China are the country’s most important grain-producing region, particularly for maize, soybean, and rice, and form its largest commercial grain base. Over the past two decades, cropping structures in this region have undergone notable shifts driven by both climate change and human activities. Generating long-term, high-resolution maps of multi-crop distribution and evaluating their suitability is essential for understanding cropping dynamics, optimizing land use, and promoting sustainable agriculture. In this study, we integrated multi-source satellite imagery from Landsat and Sentinel-2 to map the distribution of rice, maize, and soybean from 2000 to 2023 using a Random Forest classifier. A crop suitability assessment framework was developed by combining a multi-criteria evaluation model with the MaxEnt model. Reliable training samples were derived by overlaying suitability evaluation results with stable crop growth areas, and environmental variables—including climate, topography, soil, hydrology, and anthropogenic factors—were incorporated into MaxEnt to assess suitability. Furthermore, the spatial consistency between actual cultivation and suitability was evaluated to identify areas of misallocated land use. The results show that: (1) the six classification maps achieved an average overall accuracy of 91.05% and a Kappa coefficient of 0.857; (2) the cultivation area of all three crops expanded, with maize showing the largest increase, followed by soybean and rice, and the dominant conversion being from soybean to maize; (3) suitability areas ranked as soybean (376,692 km²) > maize (329,056 km²) > rice (311,869 km²), with substantial spatial overlap, particularly between maize and soybean, suggesting strong competition; and (4) in 2023, highly suitable zones accounted for 57.39% of rice, 39.69% of maize, and 28.89% of soybean cultivation, indicating a closer alignment between actual distribution and suitability for rice, weaker for maize, and weakest for soybean, whose suitable zones were often displaced by rice and maize. These findings provide insights to guide farmers in optimizing crop allocation and offer a scientific basis for policymakers in designing cultivated land protection strategies in Northeast China. Full article

In the waxy maize production of Zhejiang Province, China, conventional straw management often causes planting difficulties and nutrient competition. Although no-till with straw retention is known to benefit soil structure, its long-term impacts on local soil health and productivity remain poorly understood. Hence, a six-year field experiment (2016–2021) was conducted with four treatments, i.e., no-till with residue retention (NTRR), no-till with residue removal (NTR0), plow tillage with residue incorporation (PTRR), and plow tillage with residue removal (PTR0), to investigate the long-term effects of tillage and residue management. The results demonstrated that plow tillage (PT) significantly improved soil physical properties, reducing soil compaction and decreasing bulk density compared to no-till (NT) practices. Meanwhile, residue retention (RR) enhanced soil chemical fertility, increasing soil organic matter by 7.8–9.8% and substantially improving available potassium levels. The PTRR treatment achieved the most favorable soil conditions with the lowest compaction and bulk density values among all treatments. PTRR consistently yielded the highest maize production, showing a 1.7–6.9% advantage over PTR0 and a substantial 15.4% yield increase in spring maize compared to residue removal (R0) treatments. Correlation analyses revealed significant relationships between soil quality and productivity, with the Soil Quality Index (SQI) showing strong positive correlations with both yield (r = 0.74, p < 0.01) and economic returns (r = 0.67, p < 0.05). These findings demonstrate that PTRR represents an optimal agricultural management strategy for simultaneously enhancing soil health and ensuring sustainable crop production in fresh maize cultivation. Full article

This study assesses and inventories agrodiversity within eleven representative oases of the pre-Saharan regions of Morocco, ecosystems that are particularly vulnerable to climate change and socio-economic pressures. The findings highlight the central role of fruit tree diversity in structuring and sustaining the resilience of oasis agroecosystems, complementing cereal and fodder crops. Special attention was given to the pomegranate (Punica granatum L.), a secondary but underutilized fruit species in Moroccan agriculture, which was found to hold a significant position in the surveyed oases. Farmer and community surveys identified five local denominations or varieties, including an original form known as “Guersmoum” or “Hamed,” distinguished by its spontaneous, non-cultivated character. This unique case exemplifies the remarkable coexistence between wild and domesticated forms, reflecting the complex dynamics between cultivated and wild biodiversity. The presence and use of this variety are closely linked to the production of a traditional local agri-food product, pomegranate molasses (“Amaghousse”), an artisanal know-how transmitted across generations and primarily preserved by women. The study documents several aspects of this practice, including processing techniques, yield ratios, and marketing channels, emphasizing both the economic and cultural significance of this local product. The discussion underscores the close interconnections between traditional knowledge, gendered practices, and the conservation of genetic diversity, showing how the promotion of local resources contributes not only to the preservation of agrodiversity but also to the maintenance of oasis cultural identities. Finally, the study highlights the broader implications of these findings for development initiatives, particularly through the recognition and promotion of distinctive local agri-food products, the integration of women in local conservation strategies, and the implementation of sustainable management approaches for fruit genetic resources. Full article

Using panel data from 182 county-level cotton-growing regions in the Middle and Lower Reaches of the Yangtze River (2000–2022), this study investigates the drivers of cotton planting area contraction, focusing on the synergistic impacts of non-farm employment, agricultural policies, and their synergies, while verifying mechanisms via rural labor outflow and cotton economic returns. From a sustainability perspective, cotton planting area and output were relatively stable with fluctuations in 2000–2010, but plummeted by 80.6% and 82.8%, respectively, by 2022 (a “cliff-like” decline). Empirical results from the Spatial Durbin Model (SDM) show: (1) Non-farm employment significantly reduces local cotton cultivation and exhibits spatial spillover effects—counties neighboring or economically similar to regions with higher non-farm employment experience greater pressure for contraction; (2) This contraction is more pronounced in counties with smaller rural populations and lower cotton returns, confirming that labor scarcity and low profitability are key channels; (3) Agricultural policies exacerbate the decline: the 2005 Reward Policy for Major Grain-Producing Counties triggers cotton-to-grain substitution, while the 2014 shift from cotton temporary stockpiling to target price subsidies further accelerated the contraction of cotton cultivation in inland regions. This study contributes to understanding agricultural system transitions in the Yangtze River Basin, offering insights for optimizing sustainable planting structure adjustment and balancing food security with cash crop development under rural economic transformation. Full article

Excessive nitrogen addition in farmland on the Loess Plateau reduces soil quality and endangers the atmospheric environment. We designed an experiment to investigate the effects of different nitrogen application rates on the soil physicochemical properties and microbial diversity of spring wheat fields on the Loess Plateau, aiming to identify the optimal nitrogen application rate and avoid the detrimental effects of excessive nitrogen addition. A field experiment was conducted from 2022 to 2023 with four nitrogen (N) application rates (0, 55, 110, and 220 kg·N·ha⁻¹·y⁻¹). This study aimed to assess the changes in soil properties, nutrient contents, enzyme activities, and bacterial community structure. The results showed that increasing N application generally enhanced soil bulk density, nitrate nitrogen (NO₃⁻-N), ammonium nitrogen (NH₄⁺-N), and microbial biomass nitrogen (MBN) (p < 0.05). In contrast, soil water content initially increased and then decreased. Soil organic carbon and total nitrogen rose markedly with higher N inputs, particularly in the 0–20 cm layer, whereas total phosphorus was less affected. Nitrogen addition stimulated soil enzyme activities (protease, urease, nitrate reductase, and nitrite reductase), though excessive input (220 kg·N·ha⁻¹·y⁻¹) produced inhibitory effects. Actinobacteria (relative abundance: 29–35%) and Proteobacteria (relative abundance: 14–22%) were the dominant phyla in all treatments. Alpha diversity peaked at low nitrogen input (55 kg·N·ha⁻¹·y⁻¹), while high N level reduced evenness and species richness (p < 0.05). Principle Coordinate Analysis (PCoA) revealed that both N application and soil depth shaped microbial community assembly, with deeper layers (20–40 cm) being more sensitive to N input. Correlation analysis indicated that soil moisture, bulk density, and C:N:P stoichiometry were key drivers of bacterial community variation. Overall, moderate nitrogen input (110 kg·N·ha⁻¹·y⁻¹) improved soil fertility and supported microbial functionality, whereas excessive application degraded soil structure and reduced biodiversity. These findings highlight the need for balanced N management strategies in rain-fed agriculture of the Loess Plateau to sustain both productivity and ecological stability. Full article

The incorporation of agricultural waste into construction materials represents a promising pathway toward achieving carbon neutrality in the building sector. This study investigates the flexural performance of a novel prefabricated external wall panel composed of corn straw concrete (CSC), an eco-friendly composite material that utilizes waste corn straws. While prior studies have explored rice straw and hemp fiber concrete, they primarily focused on the mechanical properties of these materials rather than the design of prefabricated panels. This study fills the gap by optimizing reinforcement ratio and window opening layout for CSC panels, and validating their structural viability for prefabricated enclosures. An optimal mix proportion was identified, which meets the mechanical requirements for non-load-bearing applications. Four prototype panel specimens were subjected to out-of-plane monotonic loading, considering variables including reinforcement ratio (0.18% vs. 0.24%) and the presence of a window opening (25% area ratio). Results indicated that increasing the reinforcement ratio significantly enhanced the ultimate load capacity by up to 33.3% (from 45 kN to 60 kN)—an enhancement effect that was 12–15% higher than that of reported rice straw concrete. In contrast, the introduction of an opening reduced the ultimate load capacity by 11.1–16.7%. A detailed nonlinear finite element model (FEM) was developed and validated against experimental results. The validation results indicated deflection error of 7.7–12.8% (mean: 9.33%; SD: 2.05), ultimate load error of 7.7–11.1% (mean: 9.48%; SD: 1.32), and a correlation coefficient (R²) of 0.96 between simulated and experimental values. Furthermore, analytical methods for predicting the cracking moment (with an average error of 5.97%) and ultimate flexural capacity, based on yield line theory (with an average error of 8.43%), were proposed and verified. This study demonstrates the structural viability of CSC panels and provides a sustainable solution for waste reduction in prefabricated building enclosures, contributing to greener construction practices. Full article

This study introduces an IoT-enabled smart greenhouse system tailored for rice cultivation and designed as a controlled experimental platform to evaluate fertilizer application methods. Traditional greenhouse farming often struggles with unpredictable weather, pest infestations, and inefficient resource use. To overcome these challenges, the proposed system optimizes environmental conditions and enables precise monitoring and control through the Thingsboard IoT platform, which tracks temperature, humidity, and sunlight intensity in real time. The cultivation process involved Inceptisol soil preparation, slurrying, fertilization, seeding, transplantation, and continuous monitoring. The novelty lies in its dual-purpose design, enabling both cultivation and structured agronomic experimentation under identical environmental conditions. The system enables both rice cultivation and comparative testing of nano-silica fertilizer applied via root (soil) and foliar (leaf) methods. Automated temperature control (maintaining 20–36.5 °C) and humidity regulation (10–85% RH) with a mist blower response time under 5 s ensured consistent conditions. Sensor accuracy was validated with deviations of 0.4% (±0.11 °C) and 0.77% (±0.5% RH). Compared to conventional setups, this system achieved superior environmental stability and control precision, improving experimental reproducibility. Its integration potential with machine learning models opens new possibilities for forecasting plant responses based on historical data. Overall, the study demonstrates how advanced technology can enhance agricultural precision, sustainability, and research reliability. Full article

To resolve issues in the traditional agricultural production of the Ningxia irrigated area, where the sole pursuit of yield through extensive application of chemical nitrogen fertilizers has resulted in a deteriorated soil structure, reduced quality of anthropogenic-alluvial soil, and limited improvement in crop yield per unit area, a fixed-site experiment on substituting organic fertilizers for chemical nitrogen fertilizers was performed at the comprehensive experimental base of the NingXia Academy of Agriculture and Forestry Sciences during 2021–2024. Using conventional fertilization (N, P₂O₅, and K₂O application amounts of 450, 150, and 60 kg·ha⁻¹, respectively) as the control (CK), treatments of substituting organic fertilizers for 15% (T1), 30% (T2), 45% (T3), and 100% (T4) of chemical nitrogen fertilizers were used to analyze their effects on soil physical and chemical properties, as well as the maize yield in anthropogenic-alluvial soil. Substituting organic fertilizers for chemical nitrogen fertilizers increased the content of water-stable macroaggregates and the mean weight diameter (MWD) stability parameter in the soil. In 2024, the treatments of substituting organic fertilizers for chemical nitrogen fertilizers significantly increased MWD by 24.18–30.22% compared to the CK treatment. The soil’s available nitrogen content significantly decreased under the T4 treatment by 8.25–20.50% compared to CK treatment during 2021–2024. The organic matter (OM) content showed an increasing trend with the proportion of substitution of organic fertilizers for chemical nitrogen fertilizers; in 2024, the T3 and T4 treatments significantly increased OM by 5.98% and 6.60%, respectively, compared to CK. Furthermore, the available phosphorus and potassium contents also exhibited an increasing trend with the proportion of substitution of organic fertilizers for chemical nitrogen fertilizers. Based on the full dataset method, it was calculated that the T1 treatment consistently improved the soil quality index (SQI) during 2021–2024, with an increase of 9.31–18.29% compared to CK. The T1 treatment increased maize yield by 9.90% and 16.93% in 2023 and 2024, respectively, compared to CK. A random forest model identified the available nitrogen as the most critical physical and chemical indicator affecting SQI, followed by the available potassium. Linear fitting between the SQI and yield showed a highly significant positive correlation (R² = 0.6288, p < 0.01). Moreover, polynomial fitting of the proportion of substitution of organic fertilizers for chemical nitrogen fertilizers showed that SQI reached a maximum for a substitution proportion of 31.46%, while the maximum maize yield reached a proportion of 28.74%. Comprehensive analysis combining information and weight suggested an optimal proportion of substitution of organic fertilizers for chemical nitrogen fertilizers of 29.52%, achieving both an increase in SQI and maize yield in the anthropogenic-alluvial soil of the Ningxia irrigated area, while also achieving a rational utilization of organic fertilizer. Full article