Search Results (795)

Climate change and socio-economic transformation increasingly challenge the stability of China’s food supply. This study aims to optimize grain crop layouts by integrating natural suitability and nutritional supply within a unified analytical framework. Using the MaxEnt model incorporating bioclimatic, topographic, and soil variables, we simulated the natural suitability of major grain crops and compared it with actual planting patterns based on the SPAM dataset. Results revealed substantial spatial discrepancies between actual and suitable distributions, with national planting diversity index increasing by 26.42% (from 0.53 to 0.67) under suitable conditions. Wheat and maize are most suited to northern China, rice and tuber crops to southern regions, while soybean performs optimally in the northeast. Nutrient supply potential also improved substantially under the suitable scenario, with energy, protein, fat, and carbohydrate increasing by 56.9 × 10⁸ KJ, 77.2 × 10⁶ g, 23.3 × 10⁶ g, and 48.6 × 10⁶ g per million people, respectively. Among alternative structures, maize-soybean and maize-based planting structures better aligned with both natural adaptability and nutritional balance (e.g., in Inner Mongolia and Heilongjiang), whereas rice-based structure showed weaker correspondence (e.g., in Shanghai). These findings demonstrate that naturally adaptive optimization can enhance both environmental compatibility and nutritional adequacy, providing scientific guidance for developing climate-resilient and nutrition-oriented crop layout strategies in China. Full article

Sweetpotato (Ipomoea batatas) is a staple crop of strategic importance in West Africa, particularly in Côte d’Ivoire. However, its productivity is increasingly under threat due to viral diseases. Given the lack of updated epidemiological data over the past three decades, a nationwide survey was conducted in September 2023 across 94 fields in 83 locations covering seven agroecological zones of the country. A total of 221 symptomatic and asymptomatic leaf samples were analyzed using PCR for DNA viruses and RT-PCR for RNA viruses. The overall viral incidence rate calculated was 65.61%, with significant regional variations (35–97.18%, p < 0.001) and notable differences in the severity of symptoms (p = 0.0095). Agroecological zone I was the most affected, while agroecological zones IV and V were the least impacted. Four viruses were identified: cucumber mosaic virus (CMV), sweet potato leaf curl virus (SPLCV), sweet potato feathery mottle virus (SPFMV), and sweet potato chlorotic stunt virus (SPCSV). No badnaviruses were found. CMV was the most common virus found in single infections (43.44%), followed by SPLCV (5.43%). SPFMV and SPCSV were only observed in mixed infections, particularly CMV/SPLCV (14.03%) and CMV/SPFMV (1.81%). Two triple infections were also detected: SPFMV/SPCSV/CMV and SPFMV/SPLCV/CMV. In total, 34 partial coat protein sequences were obtained (28 SPLCV, 4 SPFMV, 1 CMV, 1 SPCSV). Phylogenetic analysis revealed a high similarity between SPLCV isolates characterized in Côte d’Ivoire and those from Burkina Faso, Europe (Spain, Italy), and the Americas (USA, Puerto Rico) with nucleotide identity values ranging from 98% to 100%. The Côte d’Ivoire SPCSV sequence showed 97.92% nucleotide identity with European isolates, whereas SPFMV sequences exhibited greater diversity (77–89% identity) but clustered within the West African lineage. Sweetpotato viral diseases were detected mostly in mixed-cropping fields (66.85%). This work provides the first epidemiological update on sweetpotato viral diseases since 1987 and the first molecular evidence of the nationwide presence of SPLCV and SPCSV in Côte d’Ivoire. Full article

Wheat (Triticum aestivum L.) is the most widely cultivated staple food crop globally. As a primary food source for 35–40% of the world’s population, the stability of its yield is directly linked to global food security. However, extreme weather events triggered by climate change have led to reductions in wheat yield, resulting in an urgent need to enhance the stress tolerance of wheat against drought and high temperatures. In this study, we successfully isolated and cloned a myo-inositol oxygenase gene from wheat. Further research revealed that high temperatures and drought stress significantly increased the expression level of the TaMIOXA gene in wheat leaves. A batch of overexpressing lines was obtained via Agrobacterium-mediated transformation. Compared to the control group, wheat plants with molecularly modified TaMIOXA overexpression exhibited stronger resistance to high temperatures and drought. This significantly increased their survival rates by 10% to 40%. The cumulative amount of hydrogen peroxide decreased from 7.86 × 10⁻⁴ to 1.54 × 10⁻² mmol/g, and that of malondialdehyde decreased from 8.42 × 10⁻⁷ to 2.21 × 10⁻⁶ mmol/g. This confirms that overexpression of myo-inositol oxygenase significantly enhances wheat’s tolerance to drought and high temperatures. This study offers valuable genetic resources for wheat stress tolerance. Full article

Rice, as one of the world’s three major staple crops, provides a food source for nearly half of the global population. Timely and accurate acquisition of rice cultivation information is crucial for optimizing spatial distribution, guiding production practices, and safeguarding food security. Taking Bishan District of Chongqing as the study area, NDVI values were derived from Sentinel-2 satellite imagery to construct standard NDVI time-series curves for typical land-cover types, including paddy fields, dryland, water bodies, construction land, and forest and grassland. These curves were then used in the NDVI time-series characteristics method to identify paddy fields. First, the Euclidean distance between the standard NDVI time series of paddy fields and those of other land-cover types was calculated. The sum of these element-wise differences was used to determine the upper threshold for paddy field extraction. Second, the mean absolute deviation between elements of the rice sample dataset and the standard NDVI time series was calculated for each time step. The sum of these average deviations was used as the lower threshold to extract the initial paddy field data. On this basis, an extreme-value constraint was introduced to reduce the interference of mixed pixels from forest and grassland and construction land, effectively eliminating anomalous pixels and improving the accuracy of paddy field identification. Finally, the results were validated and compared with those from other extraction methods. The results indicate that: (1) Paddy fields exhibit distinct NDVI time-series characteristics throughout the entire growing season, which can serve as a reference standard. By calculating the Euclidean distance between the NDVI curves of other land-cover types and those of paddy fields, similarity can be quantified, enabling rice identification. (2) The extraction method based on NDVI time-series characteristics successfully identified paddy fields through the appropriate setting of thresholds. The overall accuracy and Kappa coefficient remained high, while the F1-score consistently exceeded 0.8, indicating a good balance between precision and recall. Furthermore, the bootstrap uncertainty analysis revealed narrow 95% confidence intervals across all metrics, confirming the robustness and statistical reliability of the results. Overall, the proposed method demonstrated excellent performance in paddy field classification and significantly outperformed traditional machine learning methods implemented on the GEE platform. (3) Mixed pixels considerably affected the accuracy of rice classification; however, the introduction of the extreme-value constraint effectively mitigated this influence and further improved classification results. Full article

Plant pathogenic fungi pose a persistent global threat to food security, causing severe yield losses in staple crops and increasing dependence on chemical fungicides. However, the ecological and toxicological drawbacks of synthetic fungicides have intensified the search for safer, plant-derived alternatives. This review synthesizes current advances on the antifungal mechanisms of plant essential oils (EOs) and their prospects for biofungicide development. The literature reveals that the antifungal activity of EOs arises from their diverse phytochemical composition, principally terpenes, phenolics, and aldehydes that target multiple fungal cellular sites. These compounds disrupt membrane integrity through ergosterol depletion, inhibit chitin and β-glucan synthesis, interfere with mitochondrial energy metabolism, and induce oxidative stress, leading to lipid peroxidation and cell death. Morphological and transcriptomic evidence confirms that EOs alter hyphal growth, spore germination, and key gene expression pathways associated with fungal virulence. Furthermore, emerging nanotechnological and encapsulation strategies enhance EO stability, bioavailability, and field persistence, addressing major barriers to their large-scale agricultural application. The integration of EO-based biofungicides within sustainable and precision agriculture frameworks offers a promising route to reduce chemical inputs, mitigate resistance development, and promote ecological balance. This review underscores the need for interdisciplinary research linking phytochemistry, nanotechnology, and agronomy to translate EO-based antifungal mechanisms into next-generation, environmentally compatible crop protection systems. Full article

Cassava (Manihot esculenta Crantz) is the third highest-yielding source of carbohydrates among the world’s crops. In Paraguay, it is a staple food in the Paraguayan diet and the second source of starch after corn, with high demand. In this study, the percent composition of 12 cassava accessions from the germplasm bank of the Paraguayan Institute of Agricultural Technology was determined. The percent composition was determined in freeze-dried samples using the methodology of the Association of Official Analytical Chemists (AOAC), and carbohydrates were determined by difference. The results highlight that cassava is composed primarily of water and carbohydrates. It is a moderate source of dietary fiber, low in protein, and fat-free. The moisture, protein, ash, and dietary fiber contents differ significantly (p ≤ 0.01) among the cassava samples. The cassava accessions evaluated show significant variations among samples in terms of moisture, protein, ash, and dietary fiber, highlighting their diversity and the potential for differential use in food product improvement and development programs. Full article

Rice ranks among the most significant staple crops worldwide. Precise and dynamic monitoring of specific leaf area (SLA) provides essential information for evaluating rice growth and yield. While previous remote sensing studies on SLA estimation have primarily focused on crops such as wheat and soybeans, studies on rice SLA remain limited. This study aims to evaluate the predictive potential of several machine learning algorithms for estimating rice SLA across different growth stages, planting densities, and nitrogen treatments at the pre-flowering stage. By utilizing UAV-based multispectral remote sensing data, a high-precision rice SLA monitoring model was developed. The feasibility of using vegetation indices (VIs), texture indices (TIs), and their combinations to predict rice SLA was explored. VIs and TIs were derived from UAV imagery, and the recursive feature elimination was conducted on these indices individually as well as their combined fusion (VIs + TIs). Four machine learning algorithms were employed to predict SLA values. The results indicate that random forest-based models utilizing VIs, TIs, and their fusion can all predict rice SLA effectively with high accuracy. Among these models, the RF model utilizing the combined variables (VIs + TIs) exhibited the highest performance, with R² = 0.9049, RMSE = 0.0694 m²/g, RRMSE = 0.1042, and RPD = 3.2419. This study demonstrates that individual VIs can provide effective spectral information for SLA estimation, especially during the crucial pre-flowering growth phase of rice. The fusion of VIs and TIs enhances the model’s adaptability to complex field conditions by integrating both canopy biochemical and structural characteristics, thus improving model stability. This technology offers a swift and efficient approach for monitoring crop growth in the field, offering a theoretical foundation for subsequent crop yield estimation. Full article

Potato is a widely consumed staple crop prone to postharvest deterioration and quality loss. Biodegradable edible coatings offer an eco-friendly alternative to conventional packaging for extending shelf life. This study evaluated the effectiveness of whey protein concentrate (WPC) based coatings, with and without chitosan, in maintaining potato quality under different storage conditions and durations. Tubers were treated with WPC coating (WC1) or WPC–chitosan coating with additives (WC2) and stored at room temperature (RT, 24 °C), refrigeration (RF, 4 °C), or incubator (IC, 20 °C) for up to 48 days. Dry matter (DM), firmness (FR), and total soluble solids (TSS) were determined every 8 days. DM ranged between 17.3–20.7%, FR between 5.6–8.1 N, and TSS between 3.4–5.3 °Brix. Storage period (SP) had the strongest influence, with DM peaking after 16–24 days, FR gradually decreasing, and TSS dropping sharply after 32 days. Coating did not significantly affect DM, but WC2 improved FR retention while slightly lowering TSS. RF best preserved FR and TSS, whereas RT and IC accelerated quality loss. Overall, WPC-based coatings, particularly WC2, provide a biodegradable and effective strategy to reduce postharvest losses, maintain potato quality, and support sustainable food preservation. Full article

Fungal pathogens represent a major constraint to global agricultural productivity, causing a wide range of plant diseases that severely affect staple crops such as cereals, legumes, and vegetables. These infections result in substantial yield losses, deterioration of grain and produce quality, and significant economic impacts across the entire agri-food sector. Among phytopathogens, fungi are considered the most destructive, causing a wide range of diseases such as powdery mildew, rusts, fusarium head blight, smut, leaf spot, rots, late blight, and other fungal pathogens. Traditional plant protection methods do not always provide long-term effectiveness and environmental safety, which requires the introduction of innovative approaches to creating sustainable varieties. CRISPR-Cas technology opens up new opportunities for targeted genome editing, allowing the modification or silencing of susceptibility genes and thus increasing plant resistance to fungal infections. This review presents current achievements and prospects for the application of CRISPR-Cas technology to increase the resistance of major agricultural crops to fungal diseases. The implementation of these approaches contributes to the creation of highly productive and resistant varieties, which is crucial for ensuring food security in the context of climate change. Full article

As a staple food, potato (Solanum tuberosum L.) (Solanaceae) is one of the most produced food crops to ensure food security. The potato tuber moth (PTM), Phthorimaea operculella (Zeller, 1873) (Lepidoptera: Gelechiidae), is a major pest of potato, damaging both the growing and storage processes. In recent years, green pest control strategies have been gaining importance to reduce the adverse effects of chemicals and protect the environment. Entomopathogenic nematodes (EPNs) and their bacterial endosymbionts (Xenorhabdus and Photorhabdus spp.) have been one of the top topics studied in sustainable pest control approaches. In the present study, the two most common EPN species, Steinernema feltiae and Heterorhabditis bacteriophora, and their bacterial associates, Xenorhabdus bovienii and Photorhabdus luminescens subsp. kayaii were evaluated against PTM larvae separately and in combination with spinosad. The survival rates of infective juveniles (IJs) of EPNs were over 92% after 72 h of direct exposure to spinosad. Co-application of EPNs and bioactive compounds (BACs) of endosymbiotic bacteria with spinosad induced synergistic interactions and achieved the maximum mortality (100%) in PTM larvae 48 h post-treatment. Spinosad and BAC combinations were highly efficient in controlling the PTM larvae and provided LT₅₀ values below 23.0 h. Gas chromatography mass spectrometry (GC-MS) analysis identified 29 compounds in total, 20 of which belonged to P. luminescens subsp. kayaii. The results indicate that the integration of EPNs and BACs of endosymbiotic bacteria with spinosad presents a synergistic interaction and enhances pest control efficacy. Full article

Cassava (Manihot esculenta Crantz) is a globally important staple crop. Although its leaves are rich in crude protein, the protein content in its storage roots is typically less than 2%, which limits its nutritional value. Exploring high-protein storage root genotypes from germplasm collections is essential to elucidate the mechanisms underlying protein allocation, yet this remains poorly understood. Here, we conducted a three-year field evaluation of protein content in storage roots of 261 lines derived from a hybrid population (SC205*18R). It was found that there were 21 lines with high protein content that was stably above 4%. A total of 22 significant associated loci of protein content in storage roots were identified through genome-wide association analysis, with their contribution rates ranging from 0.12 to 0.35. For instance, the haplotypes of SNP-6831776 and SNP-7090537 have a prominent contribution to the protein content in the storage roots and can be used as major-effect markers in breeding. Based on this, we found 82 candidate genes, 7 of which exhibited the strongest and most consistent associations with root protein accumulation. qRT-PCR validation demonstrated that six candidate genes were significantly upregulated in high-protein varieties. These resources and findings provide a crucial foundation for breeding for storage roots with high protein and enhancing the nutritional and economic value of cassava. Full article

As a staple cereal worldwide, winter wheat plays a pivotal role in food security. Leaf chlorophyll serves as a direct indicator of photosynthetic performance and nitrogen nutrition, making it critical for precision management and yield gains. Consequently, rapid, nondestructive, and high-accuracy remote-sensing retrievals are urgently needed to underpin field operations and precision fertilization. In this study, canopy hyperspectral reflectance together with destructive chlorophyll assays were systematically acquired from Yangling field trials conducted during 2018–2020. Three families of spectral indices were devised: classical empirical indices; two-dimensional optimal spectral indices (2D OSI) selected by correlation-matrix screening; and novel three-dimensional optimal spectral indices (3D OSI). The main contribution lies in devising novel 3D OSIs that combine three spectral bands and demonstrating how their fusion with classic two-band indices can improve chlorophyll quantification. Correlation analysis showed that most empirical vegetation indices were significantly associated with chlorophyll (p < 0.05), with the new double difference index (NDDI) giving the strongest relationship (R = 0.637). Within the optimal-index sets, the difference three-dimensional spectral index (DTSI; 680, 807, and 1822 nm) achieved a correlation coefficient of 0.703 (p < 0.05). Among all multi-input fusion schemes, fusing empirical indices with 3D OSI and training with RF delivered the best validation performance (R² = 0.816, RMSE = 0.307 mg g⁻¹, MRE = 11.472%), and external data further corroborated its feasibility. Altogether, integrating 3D spectral indices with classical vegetation indices and deploying RF enabled accurate, nondestructive estimation of winter wheat chlorophyll, offering a new hyperspectral pathway for monitoring crop physiological status and advancing precision agricultural management and fertilization, can guide in-season fertilization to optimize nitrogen use, thereby advancing precision agriculture. Full article

The nourishment of the human population depends on a handful of staple crops, such as maize, rice, wheat, soybeans, potatoes, tomatoes, and cassava. However, all crop plants are affected by at least one virus causing diseases that reduce yield, and in some parts of the world, this leads to food insecurity. Conventional management practices need to be improved to incorporate recent scientific and technological developments such as antiviral gene silencing, the use of double-stranded RNA (dsRNA) to activate an antiviral response, and nanobiotechnology. dsRNA with antiviral activity disrupt viral replication, limit infection, and its use represents a promising option for virus management. However, currently, the biggest limitation for viral diseases management is that dsRNA is unstable in the environment. This review is focused on the potential of nanoparticles and nanocarriers to deliver dsRNA, enhance stability, and activate antiviral gene silencing. Effective carriers include metal-based nanoparticles, including silver, zinc oxide, and copper oxide. The stability of dsRNA and the efficiency of gene-silencing activation are enhanced by nanocarriers, including layered double hydroxides, chitosan, and carbon nanotubes, which protect and transport dsRNA to plant cells. The integration of nanocarriers and gene silencing represents a sustainable, precise, and scalable option for the management of viral diseases in crops. It is essential to continue interdisciplinary research to optimize delivery systems and ensure biosafety in large-scale agricultural applications. Full article

Zinc (Zn), a naturally occurring trace element ubiquitous in the Earth’s crust, soil, and water, is indispensable for human health due to its physiological and nutritive benefits. In this scenario, Zn is pivotal for maintaining homeostasis against toxic effects exerted by heavy metals (HMs) through bioaccumulation and metabolic interference. Zinc is an enticing cofactor for miscellaneous biochemical enzymes such as Zn metalloenzymes, which mediate crucial cellular processes, including cell proliferation, protein synthesis, immune modulation, epigenetic regulation, and nucleic acid synthesis. Recently, several research studies have focused on the thorough investigation of Zn supplementation in controlling HM toxicity by competing for binding sites and boosting protective mechanisms in humans. The current article discusses the upper limits for various toxic HMs in staple crop foods, as provided by globally recognized organizations. Clinical studies recommend a daily dose of 11 mg of Zn for healthy men and 8–12 mg for women in healthy and pregnancy conditions. However, during Zn deficiency, therapeutic supplementation is expected to be adjustable, and the dosage is increased from 15 to 30 mg daily. This review discusses the dysregulation of specific Zn importers and transporters (ZIPs/ZnTs) due to their clinical significance in immune system dysfunction as well as the progression of a myriad of cancers, including prostate, breast, and pancreas. Moreover, this review emphasizes indispensable in vitro and in vivo studies, as well as key molecular mechanisms related to Zn supplementation for treating toxicities exacerbated by HMs. Full article

Rice, as one of the world’s four major staple crops, is frequently threatened by pests and diseases during its growth. With the rapid expansion of agricultural information data, the effective management and utilization of such data have become crucial for the development of agricultural informatization. Named entity recognition technology offers precise support for the early prevention and control of crop pests and diseases. However, entity recognition for rice pests and diseases faces challenges such as structural complexity and prevalent nesting issues. Inspired by biological visual mechanisms, we propose a deep learning model capable of extracting multi-granularity features. Text representations are encoded using BERT, and the model enhances its ability to capture nested boundary information through multi-granularity convolutional neural networks (CNNs). Finally, sequence modeling and labeling are performed using a bidirectional long short-term memory network (BiLSTM) combined with a conditional random field (CRF). Experimental results demonstrate that the proposed model effectively identifies entities related to rice diseases and pests, achieving an F1 score of 91.74% on a self-constructed dataset. Full article