Search Results (16,519)

Cover crops have emerged as a crucial tool in promoting sustainable agricultural practices, particularly in improving soil quality and soil–plant health. This study investigates the impact of single cover crop plants each with varying fungal and/or bacterial symbiosis capacities in a pot experiment. The growth of non-symbiont Ethiopian mustard (Brassica carinata), the associative bacterium symbiont black oat (Avena strigosa) and the double (fungus–bacterium) endosymbiont broad bean (Vicia faba) was studied on three distinct soil types, namely a less-fertile sandy soil (Arenosol), an average value of loam soil (Luvisol) and a more productive chernozem soil (Chernozem). Beside the biomass production, nitrogen content and frequency of AM fungi symbiosis (MYCO%) of cover crops, the main soil health characteristics of electrical conductivity (EC), labile carbon (POXC) and fluorescein diacetate enzyme activity (FDA) were assessed and evaluated by detailed statistical analysis. Among the used soil types, the greatest biomass production was found on Chernozem soil with the relatively highest soil organic matter (2.81%) content and productivity. Double symbiotic activity, assessed by soil nitrogen content and mycorrhiza frequency (MYCO%), were significantly improved on the lowest-quality Arenosols (SOM 1.16%). In that slightly humous sandy soil, MYCO% was enhanced by 45%, indicating that symbiosis was crucial for plant growth in the less-fertile soil investigated. After the initial cover crop phase, the accumulated biomass was incorporated into the Luvisol (SOM 1.64%) soil, followed by the cultivation of corn (Zea mays, DK 3972) as the main crop. The results indicate that incorporating cover crop residues enhanced labile carbon (POXC) by 20% and significantly increased the FDA microbial activity in the soil, which positively correlated with the nutrient availability and growth of the maize crop. This study emphasizes the importance of selecting suitable cover crops based on their symbiotic characteristics to improve soil quality and enhance soil–plant health in sustainable agricultural systems. Full article

A small-scale organic crop producing the protected geographical indication (PGI) cultivar “Faba Asturiana”, located in northern Spain, was considered to be a case study for analyzing the environmental impacts associated with the production of this legume (Phaseolus vulgaris L.). The life cycle assessment (LCA) methodology was employed for the analysis with a “cradle-to-gate” perspective, with 1 kg of dry beans as the functional unit. The results demonstrated that the main contributor to the environmental impacts was electricity consumption (with percentages above 75% for ionizing radiation, freshwater eutrophication, terrestrial ecotoxicity, and non-carcinogenic toxicity). A carbon footprint (CF) of 1.20 kg CO₂eq per kg of dry beans was obtained (around 1000 kg CO₂/ha·y). Electricity consumption was the major contributor to the CF, followed by atmospheric emissions from waste incineration and diesel use. Furthermore, some environmental improvements were suggested, and three alternative scenarios were investigated. In conclusion, it can be established that the CF of the studied PGI bean is within the range reported by other researchers for leguminous crops. The easiest way to reduce the CF for this particular crop would be to compost the organic waste instead of burning it. Additionally, the most effective strategy would be to reduce energy consumption or use renewable energy sources. For example, if the energy supply were obtained through in situ solar production, the CF could be reduced by more than 40%. Full article

Yam (Dioscorea spp.) is a main staple tuber crop in Nigeria and the West African region. Its performance is determined by genotypes and also the environment of growth. This study assessed the agronomic performance and yield stability of elite white yam (Dioscorea rotundata) genotypes across diverse Nigerian environments. A total of 25 genotypes were evaluated at three locations in two consecutive growing seasons, 2022 and 2023, for fresh tuber yield, disease resistance, and tuber quality traits. The genotype’s performance and stability for the measured traits were assessed using various analytical tools such as additive main effects and multiplicative interaction (AMMI) and multi-trait stability index (MTSI). The AMMI analysis revealed significant differences among the genotypes and across the environments for all traits (p < 0.001, p < 0.01). The PCA revealed that the first two principal components (PC1 and PC2) explained a substantial portion of the total variation (49.84%). The MTSI identified four clones: G18, G19, G24, and G16 as promising candidates for improved yam production in Nigeria with high and stable performance for the multiple traits. Full article

Cotton, with a cultivated area of 31.92 × 10⁶ ha⁻¹ across 80 countries and an estimated annual turnover of USD 5.68 billion, is the world’s leading natural textile fiber. However, many cotton-producing countries have neglected to improve production practices, adversely affecting the environment and society. A systematic review of the sustainable cotton cultivation literature was performed for the first time to identify and suggest context-specific agricultural strategies that can be applied within different agroecosystems. The key aspects include (1) inoculation with arbuscular mycorrhizal species such as Gigaspora margarita, Funneliformis mosseae, and Acaulospora scrobiculata to enhance root exploration, biomass, and nutrient uptake; (2) using grass, legume, and brassica cover crops as a valid alternative to monoculture and fallow crop rotations to reduce resource depletion and increase the sustainability of cotton production; (3) adopting drip and mulched drip irrigation systems over traditional furrow and sprinkler systems for water conservation; (4) exploring the feasibility of prematurely terminating irrigation in humid subtropical and Mediterranean climates as an alternative to chemical defoliation without affecting cotton yield. This paper, which describes various farming practices adopted in different climates, provides farmers a guide for eco-friendly cotton agronomic management without sacrificing productivity. Full article

Plant height is a crucial indicator of crop growth. Rapid measurement of crop height facilitates the implementation and management of planting strategies, ensuring optimal crop production quality and yield. This paper presents a low-cost method for the rapid measurement of multiple lettuce heights, developed using an improved YOLOv8n-seg model and the stacking characteristics of planes in depth images. First, we designed a lightweight instance segmentation model based on YOLOv8n-seg by enhancing the model architecture and reconstructing the channel dimension distribution. This model was trained on a small-sample dataset augmented through random transformations. Secondly, we proposed a method to detect and segment the horizontal plane. This method leverages the stacking characteristics of the plane, as identified in the depth image histogram from an overhead perspective, allowing for the identification of planes parallel to the camera’s imaging plane. Subsequently, we evaluated the distance between each plane and the centers of the lettuce contours to select the cultivation substrate plane as the reference for lettuce bottom height. Finally, the height of multiple lettuce plants was determined by calculating the height difference between the top and bottom of each plant. The experimental results demonstrated that the improved model achieved a 25.56% increase in processing speed, along with a 2.4% enhancement in mean average precision compared to the original YOLOv8n-seg model. The average accuracy of the plant height measurement algorithm reached 94.339% in hydroponics and 91.22% in pot cultivation scenarios, with absolute errors of 7.39 mm and 9.23 mm, similar to the sensor’s depth direction error. With images downsampled by a factor of 1/8, the highest processing speed recorded was 6.99 frames per second (fps), enabling the system to process an average of 174 lettuce targets per second. The experimental results confirmed that the proposed method exhibits promising accuracy, efficiency, and robustness. Full article

One of the oldest oilseed crops is sesame, which is mainly cultivated due to its valuable oleic/linolenic fatty acid ratio. The application of precise irrigation and fertilisation is crucial to ensure the continuity and productivity of sesame production, especially in arid and semi-arid regions. This study aimed to determine the effect of drip irrigation and nitrogen levels on sesame’s oil and fatty acid composition. For this purpose, four nitrogen doses (N₀: 0 kg ha⁻¹, N₃₀: 30 kg ha⁻¹, N₆₀: 60 kg ha⁻¹ and N₉₀: 90 kg ha⁻¹) and three different irrigation water levels (I₅₀, I₇₅ and I₁₀₀, which correspond to 50, 75 and 100% evaporation levels from the evaporation of the Class A pan) were applied. The highest oleic acid content (43.06%) was obtained for the I₇₅N₉₀ treatment. In the case of linoleic fatty acid, the greatest value (43.66%) was for I₅₀N₀ treatment. The effects of irrigation and nitrogen doses on oleic acid and linoleic acid content were inverse of each other. An increase in applied irrigation water increased the linoleic acid content. However, it caused a decrease in oleic acid content. Increasing the nitrogen dose increased the oleic acid content and caused a decrease in linoleic acid content. Furthermore, this study showed that the I₅₀N₆₀ treatment (50% Epan and a rate of 60 kg N ha⁻¹) is the most effective for achieving high grain and oil yields in sesame cultivation. The results obtained provide practical guidance for farmers in sesame cultivation. Full article

The northern inland region of Portugal has experienced significant population decline due to the exodus of younger generations and an aging population. This has led to the abandonment of traditional activities in these territories, contributing to territorial abandonment, degradation of local economic conditions, increased social vulnerability, and a heightened risk of rural fires. The presence of communal lands, known as “baldios”, is an important facilitator for implementing actions that revitalize local villages, making them more attractive to the community. Forests, which are abundant in the baldios of northern inland Portugal, have the potential to generate environmental, social, and economic value through carbon sequestration, job creation, population stabilization, and wealth generation in the villages. However, the viability of this asset as a driver for sustainable development depends on the forest management model implemented. This case study aims to demonstrate that different forest management models have varied impacts on sustainability indicators, particularly economic and environmental sustainability. Based on naturally regenerated forests in the Carvalhelhos baldio in the Tâmega Valley region, data were collected to simulate in software four management scenarios, varying the number (0 to 4) and age of thinnings until the final cut. The simulation allowed for the calculation of the following economic indicators: Internal Rate of Return (IRR), Net Present Value (NPV), and Net Profitability Index (NPI), as well as environmental indicators related to carbon capture and accumulation, such as Gross Carbon Accumulation, Net Carbon Accumulation, Carbon accumulated in dead and suppressed trees, and carbon balance per management model. The simulations indicate that, for the studied area, Scenario 2, which involves only one thinning, yielded the highest total wood volume (cubic meters per hectare) over the cycle, making it the most suitable for biomass production. Meanwhile, Scenario 4, with three thinnings, showed the best results for individual volume (cubic meters per tree), making it more suitable for producing higher-value logs. Scenario 5 presented the best economic results and carbon capture. In all simulations, Scenario 1 showed the worst performance in the analyzed indicators. It was found that the indicators varied among the studied crop plans, highlighting that the adoption of a silvicultural regime depends on the forest characteristics, objectives, exploitation conditions, and local population sensitivity to regional priorities. Full article

Updated and accurate crop yield maps play a key role in the agricultural environment. Their application enables the support for sustainable agricultural practices and the formulation of effective strategies to mitigate the impacts of climate change. Farmers can apply the maps to gain an overview of the yield variability, improving farm management practices and optimizing inputs to increase productivity and sustainability such as fertilizers. Earth observation (EO) data make it possible to map crop yield estimations over large areas, although this will remain challenging for specific crops such as sugarcane. Yield data collection is an expensive and time-consuming practice that often limits the number of samples collected. In this study, the sugarcane yield estimation based on a small number of training datasets within smallholder crop systems in the Tha Khan Tho District, Thailand for the year 2022 was assessed. Specifically, multi-temporal satellite datasets from multiple sensors, including Sentinel-2 and Landsat 8/9, were involved. Moreover, in order to generate the sugarcane yield estimation maps, only 75 sampling plots were selected and surveyed to provide training and validation data for several powerful machine-learning algorithms, including multiple linear regression (MLR), stepwise multiple regression (SMR), partial least squares regression (PLS), random forest regression (RFR), and support vector regression (SVR). Among these algorithms, the RFR model demonstrated outstanding performance, yielding an excellent result compared to existing techniques, achieving an R-squared (R²) value of 0.79 and a root mean square error (RMSE) of 3.93 t/ha (per 10 m × 10 m pixel). Furthermore, the mapped yields across the region closely aligned with the official statistical data from the Office of the Cane and Sugar Board (with a range value of 36,000 ton). Finally, the sugarcane yield estimation model was applied to over 2100 sugarcane fields in order to provide an overview of the current state of the yield and total production in the area. In this work, the different yield rates at the field level were highlighted, providing a powerful workflow for mapping sugarcane yields across large regions, supporting sugarcane crop management and facilitating decision-making processes. Full article

Supplemental irrigation based on runoff harvesting is a sustainable solution in the current context of water scarcity that is prevalent in Sahelian countries. Runoff water harvesting ponds (RWHPs) are increasingly being utilized for vegetable cultivation by producers. This study aims to analyze the cropping choices of producers benefiting from RWHPs in the Kadiogo and Bazèga provinces of Burkina Faso in the West African Sahel. A sample of 27 surveyed producers revealed a dominant preference for vegetable crops (93.46% of the total production) over cereals. The cropping choices are influenced by factors such as the crop resistance to dry spells, the water demand, the economic return, and the market demand. For the effective utilization of the basins, crop choices should consider the water retention capacity of the basin. Additionally, to enhance the retention capacity, it is advisable to line them using appropriate waterproofing techniques. Similarly, the selection of basin installation sites should consider the soil characteristics and site-specific considerations. The findings of this research highlight the potential of runoff water harvesting basins to significantly improve agricultural productivity and resilience in the West African Sahel, thereby contributing to enhanced food security and improved livelihoods for local farmers. Full article

Crop nitrogen (N) and phosphorus (P) use efficiencies (NUE/PUE) are important to minimize wastage and nutrient pollution, but no improved crop for both is currently available. We addressed them together in rice, in the view of its high consumption of NPK fertilizers. We analyzed 46 morphophysiological parameters for the N/P response in three popular indica genotypes, namely, BPT 5204, Panvel 1, and CR Dhan 301 at low, medium, and normal N/P doses. They include 18 vegetative, 15 physiological, and 13 reproductive parameters. The segregation of significantly N/P-responsive parameters correlating with NUE/PUE revealed 21 NUE, 22 PUE, and 12 common parameters. Feature selection analyses revealed the common high-ranking parameters including the photosynthetic rate at the reproductive stage, tiller number, root–shoot ratio, culm thickness, and flag leaf width. The venn selection using the reported NUE/PUE-related candidate genes in rice revealed five genes in common for both, namely OsIAA3, OsEXPA10, OsCYP75B4, OsSultr3;4, and OsFER2, which were associated with three of the common traits for NUE/PUE. Their expression studies using qRT-PCR revealed the opposite regulation in contrasting genotypes for OsSultr3;4 and OsEXPA10 in N-response and for OsFER2 in P-response, indicating their role in contrasting N/P use efficiencies. Overall, CR Dhan 301 has the highest NUE and PUE followed by Panvel 1 and BPT5204 among the studied genotypes. Full article

Potatoes (Solanum tuberosum L.) are an important plant crop, whose yield may vary significantly depending on pedo-climatic conditions and genotype. Therefore, the analysis of the genotype × environment interaction (GEI) is mandatory for the setup of high-yielding and stable potato genotypes. This research evaluated the tuber yield (t ha⁻¹) and yield characteristic of nine potato cultivars over 3 years and 4 organic farms in Poland by additive main effects and multiplicative interactions (AMMIs) and genotype plus genotype environment interaction (GGE) biplot analyses. The results of these analyses indicated significant differentiation of tuber yield among genotypes in individual environments. It was found that the environment (E, where E = L (localization) × Y (year)), genotype (G) and GEI, but not replication, significantly affected tuber yield. The AMMI analysis showed that the environment factor explained the most considerable part of tuber yield variations (52.3%), while the GEI and G factors explained a much lower part of the variations. The AMMI and GGE analyses identified five cvs.: Twister (46.4 t ha⁻¹), Alouette (35.8 t ha⁻¹), Kokra (34.8 t ha⁻¹), Levante (33.1 t ha⁻¹), and Gardena (30.4 t ha⁻¹), as leading cultivars in the studied organic farms due to their high productivity coupled with yield stability. The statistical measure Kang (YS_i) showed that these cvs. can be considered as adaptable to a wide range of organic environments. In the case of morphological traits of tubers (tuber shape and depth of tuber eyes), the most important factor influencing both these traits was genotype (G). Influence of other factors, like localization (L), year (Y), and all interactions (double and triple), were much less significant or insignificant. In case of taste and non-darkening of tuber flesh, the main effects which significantly affected the values of these traits were genotype (G) and localization (L). We observed that cooking type can vary depending on the year (Y) and the localization (L). Full article

Sustainable agriculture faces challenges balancing the need for increasing crop production with environmental protection. Straw mulching is widely used to improve crop productivity, while reducing nitrogen application is an effective strategy to reduce greenhouse gas (GHG) emissions. However, the comprehensive evaluation of soil properties, GHG emissions, and soil quality under straw mulching conditions with different N application rates has not been extensively studied, especially in the Loess Plateau region of Central Gansu. Therefore, a two-year field experiment was conducted, involving two tillage practices (CT: conventional tillage, CTS: conventional tillage + straw mulching) and three fertilization levels (LN: low nitrogen fertilizer, 55 kg N ha⁻¹; MN: medium nitrogen fertilizer, 110 kg N ha⁻¹ HN: high nitrogen fertilizer, 220 kg N ha⁻¹). Based on the greenhouse gas emission intensity (GHGI), the minimum data set (MDS) was constructed by principal component analysis, correlation analysis, and Norm value, and the weighted index method was applied to comprehensively evaluate the soil quality. Our results showed that compared with CT, CTS significantly increased crop yields and improved the content of soil nutrients, and it was more effective when applying moderate amounts of nitrogen fertilizer. Wheat field soil behaves as a nitrous oxide (N₂O) emission source and a methane (CH₄) absorption sink. CTS–MN and CTS–LN reduced N₂O emission by 52.95–87.76%, increased total CH₄ uptake by 16.69–43.60%, and slowed down global warming potential (GWP) by 35.70–48.17% compared with CTS–HN in 2021–2022. They also reduced GHGI. Furthermore, CTS enhanced soil quality at different nitrogen application levels compared with CT, with CTS–MN exhibiting the highest Soil Quality Index (SQI) over the two years. Consequently, considering both economic and environmental benefits, CTS–MN can be regarded as a key practice for the sustainable development of agroecosystems in the Loess Plateau region of Central Gansu. Full article

A range of remote sensing platforms provide high spatial and temporal resolution insights which are useful for monitoring vegetation growth. Very few studies have focused on fruit orchards, largely due to the inherent complexity of their structure. Fruit trees are mixed with inter-rows that can be grassed or non-grassed, and there are no standard protocols for ground measurements suitable for the range of crops. The assessment of biophysical variables (BVs) for fruit orchards from optical satellites remains a significant challenge. The objectives of this study are as follows: (1) to address the challenges of extracting and better interpreting biophysical variables from optical data by proposing new ground measurements protocols tailored to various orchards with differing inter-row management practices, (2) to quantify the impact of the inter-row at the Sentinel pixel scale, and (3) to evaluate the potential of Sentinel 2 data on BVs for orchard development monitoring and the detection of key phenological stages, such as the flowering and fruit set stages. Several orchards in two pedo-climatic zones in southeast France were monitored for three years: four apricot and nectarine orchards under different management systems and nine cherry orchards with differing tree densities and inter-row surfaces. We provide the first comparison of three established ground-based methods of assessing BVs in orchards: (1) hemispherical photographs, (2) a ceptometer, and (3) the Viticanopy smartphone app. The major phenological stages, from budburst to fruit growth, were also determined by in situ annotations on the same fields monitored using Viticanopy. In parallel, Sentinel 2 images from the two study sites were processed using a Biophysical Variable Neural Network (BVNET) model to extract the main BVs, including the leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of green vegetation cover (FCOVER). The temporal dynamics of the normalised FAPAR were analysed, enabling the detection of the fruit set stage. A new aggregative model was applied to data from hemispherical photographs taken under trees and within inter-rows, enabling us to quantify the impact of the inter-row at the Sentinel 2 pixel scale. The resulting value compared to BVs computed from Sentinel 2 gave statistically significant correlations (0.57 for FCOVER and 0.45 for FAPAR, with respective RMSE values of 0.12 and 0.11). Viticanopy appears promising for assessing the PAI (plant area index) and FCOVER for orchards with grassed inter-rows, showing significant correlations with the Sentinel 2 LAI (R² of 0.72, RMSE 0.41) and FCOVER (R² 0.66 and RMSE 0.08). Overall, our results suggest that Sentinel 2 imagery can support orchard monitoring via indicators of development and inter-row management, offering data that are useful to quantify production and enhance resource management. Full article

Maize, a globally significant cereal, is increasingly cultivated under challenging environmental conditions, necessitating innovations in sustainable agriculture. This study evaluates the synergistic effects of a novel technique combining a Bacillus velezensis A6 strain with a plant extract from the Lamiales order on maize growth and stress resilience. Employing a pilot field trial, this study was conducted on the “La Añoreta” experimental farm of the ECONATUR group, where various biostimulant treatments, including bacterial and plant extract applications, were tested against a control group. The treatments were applied during key vegetative growth stages (V10-Tenth-Leaf, VT-Tassel, R1-Silking) and monitored for effects on plant height, biomass, and fumonisin content. The results suggest that the combined treatment of Bacillus velezensis A6 and the plant extract increases maize height (32.87%) and yield (62.93%) and also reduces fumonisin concentrations, improving its resistance to stress, compared to the control and other treatments. This study highlights the potential of microbial and botanical biostimulants and its novel combination for improving crop productivity and sustainability, suggesting that such synergistic combinations could play a crucial role in enhancing agricultural resilience to environmental stresses. Full article

As a significant global source of vegetable oil, the oil palm’s ability to withstand abiotic stresses, particularly drought, is crucial for sustainable agriculture. This is especially significant in tropical regions, where water scarcity is becoming more common. Nitrogen, a vital nutrient, plays an essential role in various physiological and biochemical processes in plants, directly influencing growth and stress tolerance. This study investigates the interaction between nitrogen sources (ammonium vs. nitrate) and drought stress in oil palm (Elaeis guineensis) seedlings, which is critical in enhancing productivity in this economically important crop. The experiment evaluated five commercial oil palm genotypes, which were supplied with nitrogen solutions (15 mM NH₄⁺ or NO₃⁻) for 46 days, followed by 30 days of progressive drought. The results showed that drought conditions universally reduced the biomass, with ammonium-fed plants exhibiting greater shoot biomass sensitivity than nitrate-fed plants. Drought also significantly decreased the chlorophyll a, PhiPS2, and root-reducing sugar levels—critical indicators of photosynthetic efficiency and overall plant health. The effects on the root architecture were complex, with ammonium nutrition differentially influencing the lateral root length under well-watered versus drought conditions, highlighting nitrogen forms’ nuanced role in root development. Importantly, substantial genotypic variability was observed in most traits, affecting the responses to both the nitrogen source and drought stress. This variability suggests that certain genotypes may be better suited to cultivation in specific environmental conditions, particularly drought-prone areas. In conclusion, this study underscores the intricate interplay between nitrogen nutrition, genotypic variability, and drought tolerance in oil palm seedlings. These findings highlight the need to integrate these factors into agricultural management strategies to improve resilience and productivity in oil palm plantations. Full article