Search Results (4,119)

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

With the growth of the population and the development of modern industry and the economy, the problem of heavy metal pollution in cultivated soil has become increasingly prominent. Moreover, heavy metal poses a serious threat to plant growth due to its characteristics of difficult degradation, high mobility, easy enrichment, and potential toxicity and has become a social topic. Melatonin is a new type of plant hormone widely present in animals, plants, fungi, and bacteria, and its biological role has begun investigated in the last dozen years. Facing heavy metal stress, melatonin can play a pleiotropic role in the physiological processes of plants, such as stress resistance and growth regulation, mitigate the damage caused by stress on plants, and provide a new research idea for alleviating heavy metal stress in plants. From the aspects of the plant phenotype, physiology, element absorption, and molecular structure, this paper, therefore, mainly reviews the effects of melatonin on plants subjected to heavy metal stress and the mechanism of melatonin alleviating heavy metal stress and then puts forward future research directions. This information may be of great significance to the normal growth of crops under heavy metal stress and will provide an important theoretical basis for the genetic improvement of crop resistance in the future. 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

Grafting is a commonly employed technique for enhancing the yield and improving resistance to biotic and abiotic stress of cultivated plants. However, whether and how continuous cropping of grafted plants affects the composition, function, and stability of the soil fungal community remain poorly understood. In this study, we investigated the effects of planting years (including 0 years (Y0), 2 years (Y2), 10 years (Y10), and 18 years (Y18)) of grafted watermelon on the structure and functional composition of the soil fungal community under field conditions. Compared with the Y0 soil, the Y2, Y10, and Y18 soils exhibited a significant (p < 0.05) decrease in the richness, Shannon index, and evenness (56.8–65.7%, 22.4–46.3%, and 3.8–38.1%, respectively) in the alpha diversity of the fungal community, but a significant (p < 0.05) increase (0.4–1.3 times) in the fungal population. The structure, core and unique microbiomes, and functional composition of the soil fungal community differed significantly across different planting years. The Y2, Y10, and Y18 soils exhibited significant increases (p < 0.05) in relative abundances of Ascomycota and saprophytic fungi and the proportion of core OTUs, but significantly decreased abundances of Basidiomycota, Chytridiomycota, Rozellomycota, pathogenic and symbiotic fungi, and the proportion of unique OTUs when compared with the Y0 soil. The types of potential plant pathogens and their relative abundance were also significantly increased alongside the planting years (among Y2, Y10, and Y18 soils). Furthermore, the results indicated that the continuous cropping of grafted watermelon altered the co-occurrence networks, leading to a reduction in the complexity and stability of the fungal community networks. Overall, our findings suggest that continuous cropping of grafted watermelon may adversely affect the structure and functioning of soil microbial community, eventually decreasing the effectiveness of grafting technology disease control. Full article

Neonicotinoids are a group of insecticides developed in the 1980s, reaching extensive use in agriculture in the 1990s due to their effectiveness against pests in various types of crops. In 2014, their use reached 25% of the global market. In the last decade, studies on their possible effects have been conducted, leading to bans and regulations in several European Union countries. Their persistence in soil and water can result in chronic exposure in aquatic and terrestrial organisms, including pollinator species. The accumulation of these compounds in the environment can disrupt ecosystems and affect the health of humans, plants, and animals. This review presents current knowledge on neonicotinoids, their mechanisms of action, and their transport in ecological spheres. Their presence in water and soil is evidenced, with specific concentrations reported in various regions. Their effects on non-target organisms, including aquatic animals and humans, can be negative, causing direct and indirect neurological and renal problems after exposure. More research is needed on the long-term effects on health and non-target organisms to fully understand the implications of these insecticides. Full article

Integrated crop–livestock systems (ICLS) have sustainably intensified modern agricultural practices worldwide. This research assessed how production systems and crop types impact the chemical properties of an Oxisol in the Brazilian Cerrado, the grain yield of corn intercropped with palisade grass (Urochloa) in the off-season in an ICLS, and the grain yield (GY) of soybean in succession. Intercropped and monocropped systems were assessed in a three-year field experiment: corn + Urochloa ruziziensis–soybean; corn + U. brizantha cv. Piatã–soybean; corn + U. brizantha cv. Paiaguás–soybean (ICL–Paiaguás); corn–soybean under a no-tillage system (NTS); corn–soybean under a conventional tillage system (CTS); Piatã grass–continuous grazing (Perennial Piatã); and Paiaguás grass–continuous grazing (Perennial Paiaguás). The residual impact of phosphate fertilization was more pronounced in the ICLS treatments. In the soil layer from 0.0 to 0.2 m depth, ICLS–Paiaguás and Perennial Piatã had the most positive effects on soil chemical quality. In the last year, grain yield was highest in corn monoculture under the NTS and soybean in succession under the ICLS. ICL–Paiaguás improved soil chemical properties for soybean in succession. These results confirm that an intermittent pasture system for legume crops in sequence is an alternative that can maintain or improve soil chemical composition, and that CTS should be avoided in tropical sandy soils. Full article

Turnip rape is a multi-purpose crop cultivated in temperate regions. Due to its ability to fit into crop rotation systems and its role as a food and feed source, spring-type turnip rape cultivation is on the rise. To improve the crop’s productivity and nutritional value, it is essential to understand its genetic diversity. In this study, 188 spring-type accessions were genotyped using SeqSNP, a targeted genotyping-by-sequencing method to determine genetic relationships between various groups and assess the potential effects of mutations within genes regulating major desirable traits. Single nucleotide polymorphism (SNP) alleles at six loci were predicted to have high effects on their corresponding genes’ functions, whereas nine loci had country/region-specific alleles. A neighbor-joining cluster analysis revealed three major clusters (I to III). About 72% of cluster-I accessions were of Asian origin, whereas 88.5% of European accessions and all North American accessions were placed in cluster-II or cluster-III. A principal coordinate analysis explained 65.3% of the total genetic variation. An analysis of molecular variance revealed significant differentiation among different groups of accessions. Compared to Asian cultivars, European and North American cultivars share more genetic similarities. Hence, crossbreeding Asian and European cultivars may result in improved cultivars due to desirable allele recombination. Compared to landraces and wild populations, the cultivars had more genetic variation, indicating that breeding had not caused genetic erosion. There were no significant differences between Swedish turnip rape cultivars and the NordGen collection. Hence, crossbreeding with genetically distinct cultivars could enhance the gene pool’s genetic diversity and facilitate superior cultivar development. Full article

As one of the most important staple crops in the world, rice plays a pivotal role in world food security. The creation of doubled haploids based on anther culture is an important technology for rice breeding. However, at present, rice anther culture technology still faces many problems, such as genotype dependency, especially genotypes of indica rice. In this study, fifteen rice genotypes, including twelve japonica rice genotypes and three indica rice genotypes, were randomly selected and used to study anther culture by using a modified M8 medium. The results showed that the total callus induction rates of these different rice genotypes ranged from 0.81 to 13.95%, with an average of 6.64%, while the callus induction rates calculated for the top ten highest callus inductions for each rice genotype ranged from 2.75 to 17.00%, with an average of 10.56%. There were varying gaps between the total callus induction rates and the callus induction rates in these different rice genotypes. The fact that the gaps for some rice genotypes were relatively large indicated that standard tiller or anther collection was not applicable to all rice genotypes and that there was still a lot of room for improvement in the callus induction rate of some rice genotypes through optimization of the sampling method. The plantlet regeneration rates ranged from 12.55 to 456.54%, with an average of 200.10%. Although there were many albinos from anther culture for some rice genotypes, these would still meet the requirement if the rice genotypes had higher callus induction rates or regeneration rates. The percentages of seed setting of regenerated green seedlings ranged from 14% to 84%, with an average of 48.73%. Genetic diversity analysis showed that the genetic background of these different rice genotypes was representative, and the phylogenetic tree and Principal Component Analysis (PCA) divided them into indica and japonica types. Therefore, in this study, an anther culture method suitable for both indica and japonica rice genotypes was established, which could improve doubled haploid breeding in rice. Full article

The agricultural sector is undergoing a transformative paradigm shift with the integration of advanced technologies, particularly artificial intelligence (AI), to enhance data analysis techniques and streamline decision-making processes. This paper delves into the integration of advanced technologies in agriculture, focusing specifically on optimizing data analysis through artificial intelligence (AI) to strengthen decision-making processes in farming. We present a novel AI-powered model that leverages historical agricultural datasets, utilizing a comprehensive array of established machine learning algorithms to enhance the prediction and classification of agricultural data. This work provides tailored algorithm recommendations, bypassing the need to deploy and fine-tune numerous algorithms. We approximate the accuracy of suitable algorithms, highlighting those with the highest precision, thus saving time by leveraging pre-trained AI models on historical agricultural data. Our method involves three phases: collecting diverse agricultural datasets, applying multiple classifiers, and documenting their accuracy. This information is stored in a CSV file, which is then used by AI classifiers to predict the accuracy of new, unseen datasets. By evaluating feature information and various data segmentations, we recommend the configuration that achieves the highest accuracy. This approach eliminates the need for exhaustive algorithm reruns, relying on pre-trained models to estimate outcomes based on dataset characteristics. Our experimentation spans various configurations, including different training–testing splits and feature sets across multiple dataset sizes, meticulously evaluated through key performance metrics such as accuracy, precision, recall, and F-measure. The experimental results underscore the efficiency of our model, with significant improvements in predictive accuracy and resource utilization, demonstrated through comparative performance analysis against traditional methods. This paper highlights the superiority of the proposed model in its ability to systematically determine the most effective algorithm for specific agricultural data types, thus optimizing computational resources and improving the scalability of smart farming solutions. The results reveal that the proposed system can accurately predict a near-optimal machine learning algorithm and data structure for crop data with an accuracy of 89.38%, 87.61%, and 84.27% for decision tree, random forest, and random tree algorithms, respectively. Full article

Non-grain-producing cultivated land (NGPCL) greatly affects sustainable agricultural development and food security, and its consolidation is important. With the Dujiangyan irrigation district as an example, an empirical study of NGPCL consolidation zoning was performed following the idea of “connotation definition and classification—potential identification—consolidation zoning”. On the basis of expert evaluation, NGPCL was classified into three levels according to the degree of damage to cultivated land by crop type. NGPCL was common in the study area, accounting for 53.8% of the total area. The spatial pattern of NGPCL was characterized as “continuous in the south and scattered in the north”. The assessment of theoretical and realistic NGPCL consolidation potentials suggested that areas with medium consolidation potential exhibited a contiguous distribution in the southern part of the study area, whereas it was dispersed in other regions. The proportion of area suitable for consolidation exceeded 40%. Finally, through a multiobjective optimization algorithm, a potential zoning scheme for NGPCL consolidation was constructed. The final experimental results revealed that the areas with medium or high consolidation potential accounted for 97.54% of the total area. This study is useful for supporting the governance of NGPCL. Full article

Plastic film mulching is one of the key technologies for improving agricultural productivity in arid and semi-arid regions. However, residual plastic film can severely disrupt the structure of the topsoil in farmland, leading to a decrease in crop yield. The Hexi Corridor, as the largest seed maize production base in the arid regions of Northwest China, is facing an increasingly prominent issue of residual plastic film recovery. This study designed experiments based on the typical maize planting model in the Hexi Corridor. A discrete element simulation model of the residual film–soil–root stubble complex was established using the Bonding-V2 model and API rapid filling technology. The reliability of the simulation model was verified through shear and puncture tests. The study revealed that the soil type in the Hexi Corridor is heavy sandy soil. The differences between the average maximum shear forces in the simulated and actual shear tests for root stubble–soil complexes at depths of 30 mm, 50 mm, and 100 mm were 4.8%, 6.4%, and 6.5%, respectively. Additionally, the differences in the average maximum vertical loading forces in the simulated and actual puncture tests for root stubble–soil complexes at depths of 50 mm and 100 mm were 6.4% and 12.37%, respectively. The small discrepancies between the simulated and actual values, along with the consistency of particle movement trends with real-world conditions, confirmed the reliability and accuracy of the simulation model. This indicates that the established discrete element flexible model can effectively represent actual field conditions, providing discrete element model parameters and theoretical support for optimizing the design of key components in China’s mechanized root stubble handling and residual film recovery machinery. Full article

Water is crucial for enduring horticultural productivity, but high water-use requirements and declining water supplies with the changing climate challenge economic viability, environmental sustainability, and social justice. While the scholarly literature pertaining to water management in horticulture abounds, knowledge of practices and technologies that optimize water use is scarce. Here, we review the scientific literature relating to water requirements for horticulture crops, impacts on water resources, and opportunities for improving water- and transpiration-use efficiency. We find that water requirements of horticultural crops vary widely, depending on crop type, development stage, and agroecological region, but investigations hitherto have primarily been superficial. Expansion of the horticulture sector has depleted and polluted water resources via overextraction and agrochemical contamination, but the extent and significance of such issues are not well quantified. We contend that innovative management practices and irrigation technologies can improve tactical water management and mitigate environmental impacts. Nature-based solutions in horticulture—mulching, organic amendments, hydrogels, and the like—alleviate irrigation needs, but information relating to their effectiveness across production systems and agroecological regions is limited. Novel and recycled water sources (e.g., treated wastewater, desalination) would seem promising avenues for reducing dependence on natural water resources, but such sources have detrimental environmental and human health trade-offs if not well managed. Irrigation practices including partial root-zone drying and regulated deficit irrigation evoke remarkable improvements in water use efficiency, but require significant experience for efficient implementation. More advanced applications, including IoT and AI (e.g., sensors, big data, data analytics, digital twins), have demonstrable potential in supporting smart irrigation (focused on scheduling) and precision irrigation (improving spatial distribution). While adoption of technologies and practices that improve sustainability is increasing, their application within the horticultural industry as a whole remains in its infancy. Further research, development, and extension is called for to enable successful adaptation to climate change, sustainably intensify food security, and align with other Sustainable Development Goals. Full article

Chrysanthemum, an agricultural economic crop with ornamental, medicinal, and edible values, faces the problem of continuous cropping obstacles in its cultivation. As a potential soil conditioner used to address continuous cropping obstacles (CCOs), the applicability of biochar in chrysanthemum cultivation has become a research hotspot. This study explored the effects of three different types of biochar (rice straw biochar = RB, pig manure biochar = PB, and sludge biochar = SB) on soil for chrysanthemum that had been continuously cultivated for eight years through pot experiments. The results indicate that the addition of biochar significantly reduced soil water loss. Compared with CK, the water retention rates of the SB and PB treatments increased by 25.4% and 18.4%, respectively. In the PB treatment, the contents of available phosphorus (AP) and available potassium (AK) increased by 85% and 164%, respectively. The available nitrogen (AN) content showed the highest increase under the SB treatment. All three types of biochar could improve the pH value of chrysanthemum soil with CCOs (increased by 0.4–5.4%). The results of microbial community diversity showed that, compared with CK, PB and RB slightly reduced the diversity of bacterial communities in chrysanthemum soil with CCOs (by 1.50% and 0.41%, respectively). However, the SB treatment increased the diversity of bacterial communities in chrysanthemum soil with CCOs (by 0.41%). At the same time, SB and PB significantly inhibited the diversity of fungal communities (reduced by 15.15% and 6.67%, respectively), while RB promoted the diversity of fungal communities (increased by 5.45%). Furthermore, the analysis results of bacterial phyla and genera indicated that PB and SB had enhancing effects on the beneficial bacterial phylum Actinobacteriota (8.66% and 4.64%) and the beneficial bacterial genus Nocardioides (23.29% and 9.69%). Additionally, the PB treatment enhanced the beneficial bacterial phylum Firmicutes (7.03%). The analysis results of fungal genera and phyla indicated that PB contributed to an increase in the beneficial fungal phylum Ascomycota (1.51%). RB significantly enhanced the beneficial fungal genus Chaetomium (56.34%). Additionally, all three types of biochar effectively reduced the abundance of the harmful fungal phylum Basidiomycota (30.37–73.03%). In the PB and SB treatments, the harmful fungal phylum Mucoromycota was significantly decreased (by 36.22% and 62.60%, respectively). Finally, all three types of biochar reduced the abundance of harmful fungal genera Acremonium (1.15–35.19%) and Phoma (97.1–98.7%). In this study, we investigated the effect of three kinds of biochar (RB, PB, and SB) on the soil of chrysanthemum continuous cropping through potting experiments and found that they could significantly reduce water loss, enhance water retention, increase the soil nutrient content, improve the pH value, regulate microbial communities, increase beneficial microorganisms, and reduce harmful microorganisms. These results provide a scientific basis for addressing barriers to continuous cropping (CC) while supporting the sustainability of agriculture and the development of agroecology. Full article

Crop height and biomass are the two important phenotyping traits to screen forage population types at local and regional scales. This study aims to compare the performances of multispectral and RGB sensors onboard drones for quantitative retrievals of forage crop height and biomass at very high resolution. We acquired the unmanned aerial vehicle (UAV) multispectral images (MSIs) at 1.67 cm spatial resolution and visible data (RGB) at 0.31 cm resolution and measured the forage height and above-ground biomass over the alfalfa (Medicago sativa L.) breeding trials in the Canadian Prairies. (1) For height estimation, the digital surface model (DSM) and digital terrain model (DTM) were extracted from MSI and RGB data, respectively. As the resolution of the DTM is five times less than that of the DSM, we applied an aggregation algorithm to the DSM to constrain the same spatial resolution between DSM and DTM. The difference between DSM and DTM was computed as the canopy height model (CHM), which was at 8.35 cm and 1.55 cm for MSI and RGB data, respectively. (2) For biomass estimation, the normalized difference vegetation index (NDVI) from MSI data and excess green (ExG) index from RGB data were analyzed and regressed in terms of ground measurements, leading to empirical models. The results indicate better performance of MSI for above-ground biomass (AGB) retrievals at 1.67 cm resolution and better performance of RGB data for canopy height retrievals at 1.55 cm. Although the retrieved height was well correlated with the ground measurements, a significant underestimation was observed. Thus, we developed a bias correction function to match the retrieval with the ground measurements. This study provides insight into the optimal selection of sensor for specific targeted vegetation growth traits in a forage crop. Full article

Global food security has been significantly affected by climate change; hence, there is a need to come up with lasting and adaptable agricultural practices. The objective of this study is to understand the relationships between climate-smart agriculture (CSA) and food loss management, as these are essential fields that influence sustainable agriculture. By conducting a detailed bibliometric and bibliographic analysis, we have mapped out the research landscape regarding the intersection of CSA and food loss; more importantly, we have concentrated on climate-smart strategies’ implementation for the reduction of losses all through the agricultural value chain. Our investigation combined results concerning types of crops that can survive extreme weather conditions like droughts caused by global warming or cold snaps from severe weather events. This work brought out core research directions, clusters, and the regional distribution of scholarly articles, giving an understanding of the present state of CSA and food loss study. Full article