Search Results (1,651)

Garlic constitutes a significant small-scale agricultural commodity in China. A key factor influencing garlic prices is the planted area, which can be accurately and efficiently determined using remote sensing technology. However, the spectral characteristics of garlic and winter wheat are easily confused, and the widespread intercropping of these crops in the study area exacerbates this issue, leading to significant challenges in remote sensing image analysis. Additionally, remote sensing data are often affected by weather conditions, spatial resolution, and revisit frequency, which can result in delayed and inaccurate area extraction. In this study, historical data were utilized to restore Sentinel-2 remote sensing images, aimed at mitigating cloud and rain interference. Feature combinations were devised, incorporating two vegetation indices into a comprehensive time series, along with Sentinel-1 synthetic aperture radar (SAR) time series and other temporal datasets. Multiple classification combinations were employed to extract garlic within the study area, and the accuracy of the classification results was systematically analyzed. First, we used passive satellite imagery to extract winter crops (garlic, winter wheat, and others) with high accuracy. Second, we identified garlic by applying various combinations of time series features derived from both active and passive remote sensing data. Third, we evaluated the classification outcomes of various feature combinations to generate an optimal garlic cultivation distribution map for each region. Fourth, we developed a garlic fragmentation index to assess the impact of landscape fragmentation on garlic extraction accuracy. The findings reveal that: (1) Better results in garlic extraction can be achieved using active–passive time series remote sensing. The performance of the classification model can be further enhanced by incorporating short-wave infrared bands or spliced time series data into the classification features. (2) Examination of garlic cultivation fragmentation using the garlic fragmentation index aids in elucidating variations in accuracy across the study area’s six counties. (3) Comparative analysis with validation samples demonstrated superior garlic extraction outcomes from the six primary garlic-producing counties of the North China Plain in 2021, achieving an overall precision exceeding 90%. This study offers a practical exploration of target crop identification using multi-source remote sensing data in mixed cropping areas. The methodology presented here demonstrates the potential for efficient, cost-effective, and accurate garlic classification, which is crucial for improving garlic production management and optimizing agricultural practices. Moreover, this approach holds promise for broader applications, such as nationwide garlic mapping. Full article

The leaf chlorophyll content (LCC) of winter wheat, an important food crop widely grown worldwide, is a key indicator for assessing its growth and health status in response to CO₂ stress. However, the remote sensing quantitative estimation of winter wheat LCC under CO₂ stress conditions also faces challenges such as an unclear spectral sensitivity range, baseline drift, overlapping spectral peaks, and complex spectral response due to CO₂ stress changes. To address these challenges, this study introduced the fractional order derivative (FOD) and continuous wavelet transform (CWT) techniques into the estimation of winter wheat LCC. Combined with the raw hyperspectral data, we deeply analyzed the spectral response characteristics of winter wheat LCC under CO₂ stress. We proposed a stacking model including multiple linear regression (MLR), decision tree regression (DTR), random forest (RF), and adaptive boosting (AdaBoost) to filter the optimal combination from a large number of feature variables. We use a dual-band combination and vegetation index strategy to achieve the accurate estimation of LCC in winter wheat under CO₂ stress. The results showed that (1) the FOD and CWT methods significantly improved the correlation between the raw spectral reflectance and LCC of winter wheat under CO₂ stress. (2) The 1.2-order derivative dual-band index (RVI (R₇₂₀, R₅₂₂)) constructed by combining the sensitive spectral bands of the CO₂ response of winter wheat leaves achieved a high-precision estimation of the LCC under CO₂ stress conditions (R² = 0.901). Meanwhile, the red-edged vegetation stress index (RVSI) constructed based on the CWT technique at specific scales also demonstrated good performance in LCC estimation (R² = 0.880), verifying the effectiveness of the multi-scale analysis in revealing the mechanism of the CO₂ impact on winter wheat. (3) By stacking the sensitive spectral features extracted by combining the FOD and CWT methods, we further improved the LCC estimation accuracy (R² = 0.906). This study not only provides a scientific basis and technical support for the accurate estimation of LCC in winter wheat under CO₂ stress but also provides new ideas and methods for coping with climate change, optimizing crop-growing conditions, and improving crop yield and quality in agricultural management. The proposed method is also of great reference value for estimating physiological parameters of other crops under similar environmental stresses. Full article

Fertilization significantly affects the growth and development of wheat. However, the precise mechanisms underlying gene regulation during flowering in response to fertilization deficiency remain elusive. In this study, fertilization (F) and non-fertilization (CK) ) treatments were set up to reveal examine the effect of fertilization on the photosynthetic capacity of winter wheat during the flowering period through physiological, biochemical, and transcriptome analyses. Upon analyzing analysing their yield, leaf photosynthetic system exchange parameters during flowering, antioxidant enzyme activity, and endogenous hormone parameters, we found that the F treatment resulted in higher net photosynthetic rates during flowering periods than the CK treatment. The superoxide dismutase (SOD) (83.92%), peroxidase (POD) (150.75%), and catalase (CAT) (22.74%) activities of leaves in treated with F during the flowering period were notably elevated compared to those of CK-treated leaves. Abscisic acid (ABA) (1.86%) and gibberellin acid (GA3) (33.69%) levels were reduced, whereas Auxin auxin (IAA) (98.27%) content was increasedwas increased under F treatment compared to those the results under the CK treatment. The chlorophyll a (32.53%), chlorophyll b (56%), total chlorophyll (37.96%), and carotenoid contents (29.80%) under F treatment were also increased compared to CK., exceeded exceeding those obtained under the CK treatment. Furthermore, transcriptional differences between the F and CK conditions were analyzed, and key genes were screened and validated by using q-PCR. Transcriptome analysis identified 2281 differentially expressed genes (DEGs), with enriched pathways related to photosynthesis and light harvesting. DEGs were subjected to cluster simulation, which revealed that 53 DEGS, both up- and down-regulated, responded to the F treatment. qRT-PCR-based validation confirmed the differential expression of genes associated with carbohydrate transport and metabolism, lipid transport, and signal transduction. This study revealed distinctive transcriptional patterns and crucial gene regulation networks in wheat during flowering under fertilization, providing transcriptomic guidance for the precise regulation of wheat breeding. Full article

Wheat is used for making many food products due to its diverse quality profile among different wheat classes. Since laboratory analysis of these end-use quality traits is costly and time-consuming, genetic dissection of the traits is preferential. This study used a genome-wide association study (GWAS) of ten end-use quality traits, including kernel protein, flour protein, flour yield, softness equivalence, solvent’s retention capacity, cookie diameter, and top-grain, in soft red winter wheat (SRWW) adapted to US southeast. The GWAS included 266 SRWW genotypes that were evaluated in two locations over two years (2020–2022). A total of 27,466 single nucleotide markers were used, and a total of 80 significant marker-trait associations were identified. There were 13 major-effect quantitative trait loci (QTLs) explaining >10% phenotypic variance, out of which, 12 were considered to be novel. Five of the major-effect QTLs were found to be stably expressed across multiple datasets, and four showed associations with multiple traits. Candidate genes were identified for eight of the major-effect QTLs, including genes associated with starch biosynthesis and nutritional homeostasis in plants. These findings increase genetic comprehension of these end-use quality traits and could potentially be used for improving the quality of SRWW. Full article

The salinity and alkalinity of soils are two fundamental factors that limit plant growth and productivity. For that reason, a field study conducted at Sakha Agric. Res. Station in Egypt during the 2022–2023 winter season aimed to assess the impact of gypsum (G), compost (C), and zinc foliar application in two images, traditional (Z₁ as ZnSO₄) and nanoform (Z₂ as N-ZnO), on alleviating the saline–sodic conditions of the soil and its impact on wheat productivity. The results showed that the combination of gypsum, compost, and N-ZnO foliar spray (G + C + Z₂) decreased the soil electrical conductivity (EC), sodium adsorption ratio (SAR), and exchangeable sodium percentage (ESP) by 14.81%, 40.60%, and 35.10%, respectively. Additionally, compared to the control, the G + C + Z₂ treatment showed improved nutrient content and uptake as well as superior wheat biomass parameters, such as the highest grain yield (7.07 Mg ha⁻¹), plant height (98.0 cm), 1000-grain weight (57.03 g), and straw yield (9.93 Mg ha⁻¹). Interestingly, foliar application of N-ZnO was more effective than ZnSO₄ in promoting wheat productivity. Principal component analysis highlighted a negative correlation between increased grain yield and the soil EC and SAR, whereas the soil organic matter (OM), infiltration rate (IR), and plant nutrient content were found to be positively correlated. Furthermore, employing the k-nearest neighbors technique, it was predicted that the wheat grain yield would rise to 7.25 t ha⁻¹ under certain soil parameters, such as EC (5.54 dS m⁻¹), ESP (10.02%), OM (1.41%), bulk density (1.30 g cm⁻³), infiltration rate (1.15 cm h⁻¹), and SAR (7.80%). These results demonstrate how adding compost and gypsum to foliar N-ZnO can improve the soil quality, increase the wheat yield, and improve the nutrient uptake, all of which can support sustainable agriculture. Full article

Wheat grain productivity is different from year to year because growing environments are highly seasonally variable as a result of climate change. This study analyzed the variation in the weather conditions in the 2010–2023 growing seasons and evaluated the crop developmental phase, yield-related components, and the correlations of the variables in the southern plain of South Korea, measuring agronomic traits, including the above-ground dry weight, young-panicle length, spike number per m², number of grains per spike, thousand-grain weight, and grain yield. The number of days in the heading and ripening phase showed less differences than the other growth phases. The thousand-grain weight showed low variations over the fourteen years observed, unlike the number of grains per spike, the marketable grain yield, and the straw yield, with comparatively high variations. The grain yield was negatively correlated with the average air temperature during the winter dormancy phase (R = −0.687, p = 0.007) and precipitation (R = −0.726, p = 0.003), but showed positive associations with the number of days in the winter dormancy phase (R = 0.597, p = 0.024) and the number of grains per spike (R = 0.809, p = 0.000). In conclusion, longer winter dormancy and a longer tillering phase delay young-panicle development but increase the number of spikes and the number of grains per spike, resulting in a higher wheat grain yield in Southern Korean weather conditions. Full article

The optimized sowing method and appropriate seed rate can improve wheat N use efficiency. However, the interactive effect of the sowing method and seed rate on N use efficiency, particularly N uptake and root length density, are unclear. A field experiment was conducted for two growing seasons in southern Shanxi province, China, using a split-plot design with the sowing method as the main plot (wide-belt sowing, WBS, and conventional narrow-drill sowing, NDS) and seed rate as the sub-plot (100–700 m⁻²). Our results showed that WBS had a significant and positive effect on N use efficiency (yield per unit of available N from the fertilizer and soil, by 4.7–15.4%), and the relatively higher seed rates (>300 or 400 m⁻²) enlarged the effects. The N use efficiency increases under WBS were mainly attributed to the increases in N uptake before anthesis, resulting from the promoted nodal roots per plant and per unit area, and root length density in the top layer(s). WBS promoted N translocation and the N harvest index, resulting in equivalent grain protein concentration and processing quality compared to NDS. Thus, adopting higher seed rates (>300 m⁻²) combined with WBS is recommended for achieving greater N efficiencies while maintaining the grain protein concentration and processing quality of winter wheat. Full article

Plant pathogens are an important agricultural problem, and early and rapid pathogen identification is critical for crop preservation. This work focuses on using fluorescence spectroscopy to characterize and compare healthy and fungal pathogen-infected wheat grains. The excitation–emission matrices of whole wheat grains were measured using a fluorescence spectrometer. The samples included healthy control samples and grains manually infected with Fusarium graminearum and Alternaria alternata fungi. The five distinct zones were identified by analyzing the location of the fluorescence peaks at each measurement. The zone centered at λ_em = 328/λ_ex= 278 nm showed an increase in intensity for grains infected with both pathogens during all periods of the experiment. Another zone with the center λ_em = 480/λ_ex = 400 nm is most interesting from the point of view of early diagnosis of pathogen development. A statistically significant increase of fluorescence for samples with F. graminearum is observed on day 1 after infection; for A. alternata, on day 2, and the fluorescence of both decreases to the control level on day 7. Moreover, shifts in the emission peaks from 444 nm to 452 nm were recorded as early as 2–3 h after infection. These results highlight fluorescence spectroscopy as a promising technique for the early diagnosis of fungal diseases in cereal crops. Full article

More than one-third of the global population relies on wheat as a staple food. To ultimately reduce inorganic nitrogen (N) usage through applying an organic fertilizer under drip irrigation and evaluate its effect on the yield, quality, and N utilization efficiency of winter wheat (variety Jimai 22) under various irrigation systems, an experiment was established and conducted in Yanghe Town, Jiaozhou City, from October 2020 to June 2022. The trial was designed with seven treatments, including a control (CK), to achieve a 25% total nitrogen reduction in all treatments except for CK. These treatments included drip irrigation with urea as CK, one-time application of urea through drip irrigation (FU1), one-time application of organic water-soluble fertilizer through furrow irrigation (FO1), one-time application of organic water-soluble fertilizer through drip irrigation (DO1), two-time application of organic water-soluble fertilizer through drip irrigation (DO2), one-time application of urea through drip irrigation (DU1), and two-time application of urea through drip irrigation (DU2). The results indicated that the application of a reduced N fertilizer plus an organic fertilizer significantly improved the dry matter accumulation (DMA) and the efficiency of N absorption and thus increased the grain yield. The DO2 treatment significantly exhibited a 15.5% and 16.9% increase in the DMA and the grain DMA in post-anthesis, respectively, compared to those of CK in the season of 2020–2021. Overall, the apparent nitrogen use efficiency with the drip irrigation topdressing treatments (DO1, DO2, DU1, DU2) increased significantly over two years in comparison with the urea fertilization through traditional furrow irrigation (CK), while the DO2 and DU2 treatments improved most significantly in the N use efficiency and N agronomic efficiency. Therefore, a reduced use of the inorganic N fertilizer with some organic fertilizers significantly increased the weight of thousand-grains and the yield of winter wheat, especially in the DO2 treatment, with an 11.7 t/ha and 10.9 t/ha increase, respectively, in both growing seasons of two years, while the DO2 treatment also improved the extensibility of wheat flour dough from grains harvested in both rainy (2020–2021) and less rainy (2021–2022) growing seasons. Therefore, we strongly recommend that two-time application of an organic water-soluble fertilizer through drip irrigation be the option to reduce the use of inorganic N fertilizers and increase the yield and quality of winter wheat under the conditions of this experiment. Full article

Agricultural droughts are a threat to local economies, as they disrupt crops. The monitoring of agricultural droughts is of practical significance for mitigating loss. Even though satellite data have been extensively used in agricultural studies, realizing wide-range, high-resolution, and high-precision agricultural drought monitoring is still difficult. This study combined the high spatial resolution of unmanned aerial vehicle (UAV) remote sensing with the wide-range monitoring capability of Landsat-8 and employed the local average method for upscaling to match the remote sensing images of the UAVs with satellite images. Based on the measured ground data, this study employed two machine learning algorithms, namely, random forest (RF) and eXtreme Gradient Boosting (XGBoost1.5.1), to establish the inversion models for the relative soil moisture. The results showed that the XGBoost model achieved a higher accuracy for different soil depths. For a soil depth of 0–20 cm, the XGBoost model achieved the optimal result (R² = 0.6863; root mean square error (RMSE) = 3.882%). Compared with the corresponding model for soil depth before the upscaling correction, the UAV correction can significantly improve the inversion accuracy of the relative soil moisture according to satellite remote sensing. To conclude, a map of the agricultural drought grade of winter wheat in the Huaibei Plain in China was drawn up. Full article

Water resource shortage and unreasonable application of nitrogen (N) fertilizer have been problems in wheat production of northern China. However, the interaction effects of water regimes and N practices on wheat root growth, grain yield, soil water, and inorganic N changes as well as water-N use efficiency are still unclear under drip irrigation. A field experiment was conducted during the 2020–2021 and 2021–2022 winter wheat (Triticum aestivum) growing seasons. In this study, three irrigation schedules (i.e., irrigation was applied up to 80% [D1], 75% [D2], and 70% [D3] as soon as the soil water content decreased to 65%, 60% or 55% of field capacity) and two N practices (i.e., N applied at the base, jointing, booting stages were 90, 72, 48 kg ha⁻¹ [N1], and the base, jointing, booting, filling stages were 90, 40, 40, 40 kg ha⁻¹ [N2], respectively) were considered. The decease in irrigation water amount was offset by the increase in soil water consumption. In addition, N practices significantly interacted with irrigation on soil NO₃⁻–N accumulation (2021–2022), NH₄⁺–N accumulation, SPAD value (2020–2021), N content in stems and grains at maturity, and average root length and weight density at the flowering stage. Irrigation, rather than N practices, significantly affected grain yield, total N uptake, crop N transformations (NT), the contribution of NT to grain (NTPC), water and N productivity, in which, for the value of these two seasons, D2 increased total N uptake by 18.1% (p < 0.05), and NT by 39.4% (p < 0.05) under N1 as compared to D3. Additionally, the highest WUE and ANUE were found in D2 during 2021–2022. Heavy irrigation water amount caused high a LAI; further analysis proved that the LAI was the key factor affecting grain yield, and positively and significantly correlated to yield. However, no significant difference in the LAI between D1 and D2 was found. N1 was beneficial to prevent N leaching and increase water and N use efficiency, biomass, and N transformation amount. This study recommends that D2 + N1 might be a promising system for manipulating irrigation and fertilization practices under sub-surface drip irrigation systems to improve water and N use efficiency and grain yields in semi-arid regions. Full article

To address the problems of the inaccurate seeding rate and uneven seeding in the process of dense planting of winter wheat in Xinjiang, according to the physical characteristics of the wheat seeds and the agronomic requirements of the high-yield cultivation techniques for the winter wheat “well” type, a double-hole wheel-type densely planted wheat hole sower was designed and produced. Through theoretical design and research, the structural design of the overall hole seeder and its key components was completed. The findings indicated that 5–7 wheat seeds could be planted in each hole at a 9.2 mm nest depth and 610 mm³ nest volume, which was consistent with the “well”-type high-yield dense-planting cultivation technology’s need for 400,000–500,000 basic seedlings per mu. The rotation speed and the quantity of the wave guide teeth were used as test factors and the qualifying index, replay index, and missed sowing index were used as test indicators to create the two-factor, three-level central composite design center combination test. It was possible to derive the mathematical model connecting the test factors and test indexes. The regression model underwent multi-objective optimization using the Design-Expert 13 program to determine the optimal parameters: the qualifying index was 91.24%, the replay index was 6.14%, and the missed seeding index was 2.62% when the wave guide rail had four teeth and the seed drill rotated at a speed of 40 revolutions per minute. The best parameter combinations were used for a bench verification test, and the test indicated that the qualified index was 90.25%, the replay index was 4.59%, and the missed broadcast index was 5.16%. The results demonstrated that the densely planted wheat hole seeder performs well, satisfies the requirements for winter wheat dense-planting and sowing operations, and serves as a model for the densely planted wheat hole seeders that will be optimized in the future. Full article

Diversifying crop rotations by incorporating legumes is recommended to enhance the resilience of agricultural systems against environmental stresses and optimize nitrogen utilization. Nonetheless, ploughing forage legumes or grass-legumes poses a significant risk of nitrate leaching. The study aimed to assess the impact of strip tillage intercropping management on soil mineral nitrogen, water-extractable organic carbon, mobile humic substances content, and winter wheat (Triticum aestivum L.) grain yield compared to forage legume and winter wheat monocropping with conventional tillage. In the intercropping systems, the following bicrops were used: black medick (Medicago lupulina L.) with winter wheat, white clover (Trifolium repens L.) with winter wheat, and Egyptian clover (Trifolium alexandrinum L.) with winter wheat. Research was conducted in two experiments. The results indicated that after implementing strip tillage and winter wheat intercropping, the soil mineral nitrogen content was similar to or lower than that observed in conventional tillage and winter wheat sowing after forage legumes. Winter wheat grain yield in intercrops decreased compared to the legumes monocultures that were ploughed before winter wheat sowing. The highest amount of water- extractable organic carbon was in intercropping growing white clover and winter wheat bicrops or in all fields (except Egyptian clover and winter wheat bicrops) after applying strip tillage. During the research period, the quantities of mobile humic substances and mobile humic acids exhibited similar changes. Their content increased substantially in fields with white clover and Egyptian clover, regardless of whether the legumes were ploughed or grown with winter wheat. Full article

Farmers in Kazakhstan face unreliable water resources. This includes water scarcity in the summer, high fluctuations in precipitation levels, and an increase in extreme weather events such as snow, rain, floods, and droughts. Wheat production is regulated and subsidized by the Kazakh government to strengthen food security. The proper monitoring of crop production is vital to government agencies, as well as insurance and banking structures. These organizations offer subsidies through different levels support. Some farmers already use farmland soil monitoring combined with adaptive combinations of different crops. These include winter–spring plowing crop programs. Winter wheat crops are generally more adaptive and may survive summer droughts. Kazakhstan is a large country with large plots of farmland, which are complicated to monitor. Therefore, it would be reasonable to adapt more efficient technologies and methodologies, such as remote sensing. This research work presents a method for identifying winter wheat crops in the foothills of South Kazakhstan by employing multi-temporal Sentinel-2 data. Here, the researchers adapted and applied a Plowed Land Index, derived from the Brightness Index. The methodology encompasses satellite data processing, the computation of Plowed Land Index values for the swift recognition of plowed fields and the demarcation of winter wheat crop sowing regions, along with a comparative analysis of the acquired data with ground surveys. Full article

Accurate yield prediction is essential for global food security and effective agricultural management. Traditional empirical statistical models and crop models face significant limitations, including high computational demands and dependency on high-resolution soil and daily weather data, that restrict their scalability across different temporal and spatial scales. Moreover, the lack of sufficient observational data further hinders the broad application of these methods. In this study, building on the SCYM method, we propose an integrated framework that combines crop models and machine learning techniques to optimize crop yield modeling methods and the selection of vegetation indices. We evaluated three commonly used vegetation indices and three widely applied ML techniques. Additionally, we assessed the impact of combining meteorological and phenological variables on yield estimation accuracy. The results indicated that the green chlorophyll vegetation index (GCVI) outperformed the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) in linear models, achieving an R² of 0.31 and an RMSE of 396 kg/ha. Non-linear ML methods, particularly LightGBM, demonstrated superior performance, with an R² of 0.42 and RMSE of 365 kg/ha for GCVI. The combination of GCVI with meteorological and phenological data provided the best results, with an R² of 0.60 and an RMSE of 295 kg/ha. Our proposed framework significantly enhances the accuracy and efficiency of winter wheat yield estimation, supporting more effective agricultural management and policymaking. Full article