Search Results (253)

As an important reserve resource for cultivated land, the improvement and fertility enhancement of saline-alkali land are key to alleviating the pressure on cultivated land and ensuring the sustainable utilization of land resources. Studying the regulatory effect of rotation patterns on the soil fertility of saline-alkali land is one of the core research contents in exploring low-cost and environmentally friendly comprehensive management strategies for saline-alkali land. This study focuses on Zhaoyuan County, a representative saline and alkaline area within the Songnen Plain. Utilizing remote sensing technology, crop information was systematically collected across 13 time periods spanning from 2008 to 2020. These data were employed to construct a comprehensive crop information change atlas. This atlas categorized crop rotation patterns based on crop combinations, rotation frequencies, and the number of consecutive years of planting. Using soil sampling data from 2008 and 2020, a soil fertility evaluation was conducted, and the changes in soil chemical properties and fertility under various crop rotation patterns were analyzed. The results of the study show that, during the study period, crop rotation patterns in Zhaoyuan County were dominated by paddy-upland rotations and upland crop rotations. Crop rotation patterns, categorized by crop combination, were dominated by soybean–maize–other crops rotation (S-M-O) and rice–soybean–maize–other crops rotation (R-S-M-O). The frequency of crop rotation is dominated by low- and medium-frequency crop rotation. Crop rotation significantly increased soil organic matter, total nitrogen content, and overall soil fertility in the study area, while simultaneously lowering soil pH levels. Crop rotation patterns with different crop combinations had significant effects on soil chemical properties, with smaller differences in the effects of different rotation frequencies and years of continuous cropping. Crop rotation patterns incorporating soybean demonstrate a significant positive regulatory impact on the soil fertility of saline-alkali land. Low-frequency crop rotation (with ≤5 crop changes) has a relatively better effect on improving soil fertility. This research provides important empirical support and decision-making references for establishing sustainable farming systems in ecologically fragile saline-alkali areas, ensuring regional food security, and promoting the long-term sustainable utilization of land resources. Full article

Potato (Solanum tuberosum L.) is one of the most important food crops in the world, ranking fourth after rice, maize, and wheat. Potatoes are exposed to biotic and abiotic environmental factors, which lead to economic losses and increase the possibility of food security threats in many countries. Traditional potato breeding faces several challenges, primarily due to its genetic complexity and the time-consuming nature of the process. Therefore, gene editing—CRISPR-Cas technology—allows for more precise and rapid changes to the potato genome, which can speed up the breeding process and lead to more effective varieties. In this review, we consider CRISPR-Cas technology as a potential tool for plant breeding strategies to ensure global food security. This review summarizes in detail current and potential technological breakthroughs that open new opportunities for the use of CRISPR-Cas technology for potato breeding, as well as for increasing resistance to abiotic and biotic stresses, and improving potato tuber quality. In addition, the review discusses the challenges and future perspectives of the CRISPR-Cas system in the prospects of the development of potato production and the regulation of gene-edited crops in different countries around the world. Full article

Accurate detection of maize tassels plays a crucial role in yield estimation of maize in precision agriculture. Recently, UAV and deep learning technologies have been widely introduced in various applications of field monitoring. However, complex field backgrounds pose multiple challenges against the precision detection of maize tassels, including maize tassel multi-scale variations caused by varietal differences and growth stage variations, intra-class occlusion, and background interference. To achieve accurate maize tassel detection in UAV images under complex field backgrounds, this study proposes an MSMT-RTDETR detection model. The Faster-RPE Block is first designed to enhance multi-scale feature extraction while reducing model Params and FLOPs. To improve detection performance for multi-scale targets in complex field backgrounds, a Dynamic Cross-Scale Feature Fusion Module (Dy-CCFM) is constructed by upgrading the CCFM through dynamic sampling strategies and multi-branch architecture. Furthermore, the MPCC3 module is built via re-parameterization methods, and further strengthens cross-channel information extraction capability and model stability to deal with intra-class occlusion. Experimental results on the MTDC-UAV dataset demonstrate that the MSMT-RTDETR significantly outperforms the baseline in detecting maize tassels under complex field backgrounds, where a precision of 84.2% was achieved. Compared with Deformable DETR and YOLOv10m, improvements of 2.8% and 2.0% were achieved, respectively, in the mAP₅₀ for UAV images. This study proposes an innovative solution for accurate maize tassel detection, establishing a reliable technical foundation for maize yield estimation. Full article

Soil available nitrogen (AN) is a critical nutrient for plant absorption and utilization. Accurately mapping its spatial distribution is essential for improving crop yields and advancing precision agriculture. In this study, 188 AN soil samples (0–20 cm) were collected at Heshan Farm, Nenjiang County, Heihe City, Heilongjiang Province, in 2023. The soil available nitrogen content ranged from 65.81 to 387.10 mg kg⁻¹, with a mean value of 213.85 ± 61.16 mg kg⁻¹. Sentinel-2 images and normalized vegetation index (NDVI) and enhanced vegetation index (EVI) time series data were acquired on the Google Earth Engine (GEE) platform in the study area during the bare soil period (April, May, and October) and the growth period (June–September). These remote sensing variables were combined with soil sample data, crop type information, and crop growth period data as predictive factors and input into a Random Forest (RF) model optimized using the Optuna hyperparameter tuning algorithm. The accuracy of different strategies was evaluated using 5-fold cross-validation. The research results indicate that (1) the introduction of growth information at different growth periods of soybean and maize has different effects on the accuracy of soil AN mapping. In soybean plantations, the introduction of EVI data during the pod setting period increased the mapping accuracy R² by 0.024–0.088 compared to other growth periods. In maize plantations, the introduction of EVI data during the grouting period increased R² by 0.004–0.033 compared to other growth periods, which is closely related to the nitrogen absorption intensity and spectral response characteristics during the reproductive growth period of crops. (2) Combining the crop types and their optimal period growth information could improve the mapping accuracy, compared with only using the bare soil period image (R² = 0.597)—the R² increased by 0.035, the root mean square error (RMSE) decreased by 0.504%, and the mapping accuracy of R² could be up to 0.632. (3) The mapping accuracy of the bare soil period image differed significantly among different months, with a higher mapping accuracy for the spring data than the fall, the R² value improved by 0.106 and 0.100 compared with that of the fall, and the month of April was the optimal window period of the bare soil period in the present study area. The study shows that when mapping the soil AN content in arable land, different crop types, data collection time, and crop growth differences should be considered comprehensively, and the combination of specific crop types and their optimal period growth information has a greater potential to improve the accuracy of mapping soil AN content. This method not only opens up a new technological path to improve the accuracy of remote sensing mapping of soil attributes but also lays a solid foundation for the research and development of precision agriculture and sustainability. Full article

The combined application of chemical fertilizers with organic materials contributes to higher contents of bioavailable phosphorus. However, the underlying mechanism remains poorly understood. A field experiment including four treatments, chemical fertilizer (CF), chemical fertilizer with biochar (CB), chemical fertilizer with organic fertilizer (CO), and chemical fertilizer with biochar and organic fertilizer (CBO), was conducted to explore how the combination of fertilizer applications enhanced soil phosphorus bioavailability using metagenomic sequencing technology. The results showed that chemical fertilizers combined with organic materials (CB, CO, and CBO) significantly increased citrate-extractable phosphorus by 34.61–138.92% and hydrochloric acid-extractable phosphorus contents by 72.85–131.07% compared to CF. In addition, the combined applications altered the microbial community structure and increased the abundance of phoR, spoT, and ppnK genes, but decreased those of gcd, phoD, and ppk1 genes. A partial least squares path model indicated that the combined applications regulated the microbial community composition and gene abundance of phosphorus-cycling microorganisms by influencing soil physicochemical properties, thereby enhancing soil phosphorus cycling. Correlation analysis indicated that pH, total phosphorus, and available phosphorus were the key factors influencing microbial communities, while available nitrogen and total nitrogen primarily regulated phosphorus cycling gene abundance. In addition, the CO and CBO treatments significantly increased maize yield by 14.60% and 21.04%, respectively. Overall, CBO most effectively enhanced bioavailable phosphorus content and maize yield. This study provides a foundation for developing rational fertilization strategies and improving soil phosphorus use efficiency. Full article

Maize is a vital global crop, serving as a fundamental component of global food security. To support sustainable maize production, the accurate classification of maize seeds—particularly distinguishing haploid from diploid types—is essential for enhancing breeding efficiency. Conventional methods relying on manual inspection or simple machine learning are prone to errors and unsuitable for large-scale data. To overcome these limitations, we propose Slot-Maize, a novel deep learning architecture that integrates Convolutional Neural Networks (CNN), Slot Attention, Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM) layers. The Slot-Maize model was evaluated using two datasets: the Maize Seed Dataset and the Maize Variety Dataset. The Slot Attention module improves feature representation by focusing on object-centric regions within seed images. The GRU captures short-term sequential patterns in extracted features, while the LSTM models long-range dependencies, enhancing temporal understanding. Furthermore, Grad-CAM was utilized as an explainable AI technique to enhance the interpretability of the model’s decisions. The model demonstrated an accuracy of 96.97% on the Maize Seed Dataset and 92.30% on the Maize Variety Dataset, outperforming existing methods in both cases. These results demonstrate the model’s robustness, generalizability, and potential to accelerate automated maize breeding workflows. In conclusion, the Slot-Maize model provides a robust and interpretable solution for automated maize seed classification, representing a significant advancement in agricultural technology. By combining accuracy with explainability, Slot-Maize provides a reliable tool for precision agriculture. Full article

Organic agriculture is widely regarded as an important approach to reducing biodiversity loss and promoting sustainable agricultural development compared to conventional agriculture. Notably, organic farming practices have substantially boosted the diversity of soil microbial communities. However, empirical studies on the functional structure of soil microbial communities in organic agroecosystems and the mechanisms influencing them remain relatively scarce. Using high-throughput sequencing technology, we analyzed soil microbial communities associated with organic (orange lands) and conventional (coffee and maize lands) farming practices in the Gaoligong Mountains (GLGM) region, with the aim of revealing differences in soil properties, microbial community structure, and functional composition across different agricultural management practices. The results revealed that organic farming boosted soil organic carbon and fertility, driving changes in the microbial community composition. Organic farming notably increased the abundance of bacterial functional groups involved in the carbon and nitrogen cycles but decreased the abundance of symbiotic fungi. Furthermore, no significant differences were observed in the abundance of saprotrophic and pathogenic fungi between the organic and conventional farming systems. The present study demonstrates that organic farming enhances the functional roles of oil microorganisms in nutrient cycling and overall ecosystem processes by enhancing soil’s organic carbon content and soil fertility, thereby modifying the soil’s microbial community structure and functions. Overall, organic farming contributes to improvements in soil health and supports the sustainable development of agriculture in the GLGM region. Full article

Zinc deficiency is a major contributor to hidden hunger, affecting billions of people worldwide, particularly in vulnerable populations. Agronomic biofortification with zinc is a promising strategy to increase both crop productivity and the nutritional quality of food, especially in countries like Brazil, where tropical soils are often deficient in this micronutrient. This review analyzes the main technologies applied in the zinc biofortification of edible crops in Brazil, including fertilizer types, application methods, doses, and the use of innovative approaches such as nano-fertilizers and biofertilizers. The results show that the foliar application of zinc sulfate at doses of 600 g ha⁻¹ increased zinc concentration in grains by 25–40% without reducing crop yields. Additionally, the use of zinc nanoparticles increased wheat grain zinc content by up to 30% and biomass production, while biofertilizer application with diazotrophic bacteria raised zinc concentration in maize grains by 12.7–18.2%. These technologies demonstrate potential for enhancing zinc use efficiency and improving the nutritional quality of crops. Standardizing biofortification practices is essential to maximize their impact on food and nutritional security, contributing to the prevention of zinc deficiency in human populations. Full article

Maize (Zea mays) is one of the most cultivated crops in South Africa, serving as a staple food, stock feed, and a key element in several industrial applications. It contributes significantly to the growth of the South African agricultural economy. The cultivation of maize generates a large amount of agricultural waste, mainly in the form of maize stover (MS), which encapsulates leaves, stalks, cobs, and husks. Approximately 5.15 metric tons (Mt) yr⁻¹ of MS are generated in South Africa. This corresponds to an energy potential of 94 PJ. There is immense potential to surpass the annual yield of MS by 126% up to about 11.66 Mt yr⁻¹ through practices such as zero tillage and improved agricultural production systems. MS may pose a serious threat to the environment if not managed in a sustainable and eco-friendly manner. Valorization of MS into biogas presents an excellent opportunity to effectively control biomass waste while contributing to renewable energy production and mitigating dependence on depleting fossil fuels. However, MS continues to be overlooked as a sustainable bioenergy resource due to its lignocellulosic structure. This study explores the potential of converting MS into biogas for heat and power generation, addressing both energy needs and waste management in South Africa. The purpose is to provide knowledge that will inform researchers, innovators, industrialists, policy makers, investors, and other key stakeholders interested in renewable energy systems. Collaborative efforts among multiple stakeholders are vital to leverage biogas as a technology to promote socio-economic development in South Africa. Full article

Maize (Zea mays L.) is a staple cereal crop worldwide, but its productivity is significantly affected by extreme weather conditions such as drought and heat stress. Plant growth, physiological processes, and yield potential are all affected by these conditions; as such, resilient maize crops are required to tackle these abiotic challenges. With an emphasis on morphological, physiological, and biochemical reactions, this review paper investigates the processes that underlie resistance to certain environmental challenges. Features including deep root systems, osmotic adaptations, and antioxidant enzyme activity help maize withstand drought. Activation of drought- and heat-responsive genes, accumulation of osmoregulatory compounds, and changes in membrane fluidity are all components of abiotic stress tolerance. Likewise, improved transpiration efficiency, modified photosynthetic processes, and improved heat shock proteins are used to produce heat resistance. Enhancing resilience requires progress in breeding methods, genetic engineering, and agronomic techniques, such as the use of stress-tolerant cultivars, biotechnology interventions, and climate-smart agriculture tactics. A special focus was given to cutting edge technologies like CRISPER-Cas9-mediated recent advances in heat and drought resistance. This review sheds light on recent studies and potential avenues for enhancing resilience to harsh climatic conditions, guaranteeing food security in the face of climate change. Full article

Field mapping is one of the most important aspects of precision agriculture, and community drones will be able to empower young rural entrepreneurs who will be the generational replacement of a new agrosocial paradigm. This research presents an agroecological participatory innovation methodology that utilizes precision technology through geographic information systems and unmanned aerial vehicles to evaluate the integrated ecological management of weeds for glyphosate substitution in a transitional area of Citrus sinensis in San Luis Potosí, Mexico. Modeling methods and spatial analyses supported by intelligent georeference protocols were used to determine the number of weeds with tolerance and glyphosate resistance. Four control flights were conducted to monitor seven treatments. Glyphosate-resistant weeds were represented with the highest number of individuals and frequency in all experimental treatments. Although the treatment with maize stubble showed a slightly better result than the use of Mucuna pruriens mulch, which prevents the emergence of glyphosate resistant weeds before emergence, the second treatment is considered better in terms of the cost–benefit ratio, not only because of significantly lower cost but also because of the additional benefits it offers. Geospatial technologies will determine the nature of citrus and fruit tree agroecological treatments and highlight areas of the plot with binomial soil and plant nutrient deficiencies and pest and disease infestations, which will improve the timely application of bio-inputs through the development of accurate maps of agroecological transitions. Full article

LoRa, as a leading LPWAN technology, plays a pivotal role in enabling long-range, low-power wireless communication, especially in agricultural IoT applications. This study examines the propagation characteristics of 433 MHz LoRa signals in maize fields, focusing on signal attenuation, RSSI, SNR, and packet loss under dense crop conditions. Field experiments were conducted in Wuwei, Gansu Province, with validation tests in Tongliao, Inner Mongolia. The effects of transmitter and receiver antenna heights on signal quality and propagation distance were systematically analyzed. Results show a consistent improvement in signal quality and range with increased antenna height. Path loss models were developed using regression analysis, achieving high predictive accuracy (R² > 0.9). Validation confirmed the models’ reliability, offering valuable insights for deploying wireless sensor networks (WSNs) in agriculture. Future research will integrate machine learning for dynamic modeling and explore variations across crop growth stages. Full article

Maize is a vital staple crop significantly affected by climate change, necessitating urgent efforts to enhance its resilience. This review analyzes advanced methodologies for maize improvement, focusing on the identification of genetic determinants through QTL mapping, candidate gene mining, and GWAS. We highlight the transformative potential of CRISPR gene editing for identifying key regulators in maize development and the utility of virus-induced gene silencing (VIGS) for functional genomics. Additionally, we discuss breeding strategies leveraging the genetic diversity of maize wild relatives and innovations such as speed breeding and genomic selection (GS), which accelerate breeding cycles. Marker-assisted selection (MAS) plays a critical role in developing superior maize varieties. The review also encompasses agronomic practices and technological innovations, including GS, aimed at climate mitigation. High-throughput phenotyping and omics-based approaches, including transcriptomics and metabolomics, are essential tools for developing climate-resilient maize. Climate changes have a significant impact on maize production and pose unprecedented challenges to its cultivation. Full article

The current rising food prices, influenced by importation costs, the global food crisis, as well as pre- and post-harvest losses, have contributed majorly to malnutrition and food insecurity. Therefore, utilizing technologies that harness our indigenous agro-products as composite flours to develop functional foods will address these issues. In this study, dry raw samples of perishable and healthy yellow potato, yellow maize and pigeon pea were obtained from the agricultural development program, Edo State, Nigeria, and authenticated and processed into gluten-free fermented composite flours. The flours were profiled physicochemically and nutritionally, providing valuable insight into their multiple benefits. An experimental design software (Design Expert 13.0.) was applied to achieve optimum blended flours regarding the ratio of sweet potato–pigeon pea–maize, and mix 5 (67.70:20.00:12.31) displayed more outstanding attributes than other blends for the production of biscuits, bread and cakes using creaming and mixing methods. Various standard tests for flours and products were appropriately carried out to evaluate the proximate, techno-functional, mineral, antioxidant, anti-nutrient, sensory and color values. Individual antioxidant parameters were improved across all products compared to wheat-based products (control) under the same production conditions, showing a statistical significance at p < 0.05. A similar trend was observed in the proximate, anti-nutritional and mineral contents, while all products had a desirable color outlook. A sensory evaluation revealed the general acceptability, while an in vivo animal experimental model revealed that all animals fed with the various product samples gained weight with improved general body organs and no evidence of disease. This research underscores the potential of harnessing agri-value chain approaches in developing functional foods and promoting food security. Full article

Appropriate fertilization depth promotes the absorption and transport of nutrients, crop growth and yield. However, little is known about whether deep fertilization improves crude protein synthesis and how to regulate it. A two-year field experiment was conducted with various fertilization depths: (1) conventional fertilization (CF), (2) fertilization application depth at 30 cm (DF), and (3) fertilizer average application at depths of 15 cm and 30 cm (AF). The fertilization rates under all treatments were 300 kg N ha⁻¹ nitrogen fertilizer (urea, 46% N), 150 kg P₂O₅ ha⁻¹ calcium superphosphate (16% P₂O₅), and 135 kg K₂O ha⁻¹ potassium sulfate (51% K₂O). The nitrogen/potassium (N/K) ratio, the activities of nitrate reductase [NR], glutamine synthetase [GS], and glutamic pyruvic transaminase [GPT], crude protein content in leaves, stems, and grains, as well as the relationships among the parameters were explored. The result showed that deep fertilization (DF) significantly improved the N/K ratio. NR activity in DF increased by 26.30%, 35.56%, and 57.30% in leaves, stems, and grains, respectively, when compared to conventional fertilization (CF), and by 54.22%, 43.27%, and 28.44% when compared to average fertilization (AF). GS activity in DF increased by 29.67%, 47.96%, and 47.46% in leaves, stems, and grains when compared to CF, and by 40.05%, 31.51%, and 40.62% when compared to AF. GPT activity in DF was significantly higher than CF and AF in grains, and differences between treatments were significant. Crude protein content was significantly correlated with NR and GS activities in leaves, GPT activity in stems, as well as GS and GPT activities in grains. The crude protein content of leaves and grains in DF was significantly higher than in CF and AF. In conclusion, DF significantly improved crude protein synthesis and increased the crude protein content of forage maize by increasing the whole plant N/K ratio, NR and GS activities in leaves, as well as GS and GPT activities in grains. It is a highly efficient cultivation technology that significantly improves the quality of forage maize. Full article