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The design and technological scheme of a small-sized forage harvester with a capture width of 1.35 m equipped with a device oriented along the length of the stems was developed in this study. As a result of theoretical studies, the process of the movement of mass into the chamber of the mowing rotor due to centrifugal forces was revealed. The speed of mass movement and the average size of crushed particles with the mowing rotor were determined. The oriented feeding process of stems in the chamber of the chopping rotor is mathematically described in this paper. An analytical expression is obtained for determining the average size of crushed particles by the forage harvester, that is, a mathematical model of the processes of mowing, oriented feeding, and the chopping of stem fodder by the forage harvester. Laboratory and field tests of a forage harvester equipped with a device oriented along the length of the stems were conducted. The combine harvester’s productivity was 6.14 t/h when mowing alfalfa. Special experiments were conducted to determine the average size of crushed particles after the mowing rotor. The average size of crushed particles with the mowing rotor was 147.4 mm, while the theoretical value was 144 mm. The difference between these values was only 2.31%. A special experiment was conducted on the combine without an orienting device to compare the quality indicators. The mass fractions of crushed particles of up to 50 mm in length when the combine was operating with and without an orienting device were 79.3 and 46.7, respectively. Accordingly, the average length of crushed particles was 33.79 mm and that without an orienting device was 77.07 mm. The theoretical value of the average length of crushed particles was 34.9 mm (i.e., the difference between the theoretical and actual value of the average size of the crushed particles was only 3.25%). All this proves that when the combine harvester was operated with an orienting device, there was a significant increase in the quality indicators of the chopped feed, and the reliability of the theoretical studies and the resulting mathematical model were determined.

Accurate ear counting is essential for determining wheat yield, but traditional manual methods are labour-intensive and time-consuming. This study introduces an innovative approach by developing an automatic ear-counting system that leverages machine learning techniques applied to high-resolution images captured by unmanned aerial vehicles (UAVs). Drone-based images were captured during the late growth stage of wheat across 15 fields in Bosnia and Herzegovina. The images, processed to a resolution of 1024 × 1024 pixels, were manually annotated with regions of interest (ROIs) containing wheat ears. A dataset consisting of 556 high-resolution images was compiled, and advanced models including Faster R-CNN, YOLOv8, and RT-DETR were utilised for ear detection. The study found that although lower-quality images had a minor effect on detection accuracy, they did not significantly hinder the overall performance of the models. This research demonstrates the potential of digital technologies, particularly machine learning and UAVs, in transforming traditional agricultural practices. The novel application of automated ear counting via machine learning provides a scalable, efficient solution for yield prediction, enhancing sustainability and competitiveness in agriculture.

Ensuring the quality of medicinal herbs in the herbal market is crucial. However, the genetic and physical similarities among medicinal materials have led to issues of mixing and counterfeit distribution, posing significant challenges to quality assurance. Recent advancements in deep learning technology, widely applied in the field of computer vision, have demonstrated the potential to classify images quickly and accurately, even those that can only be distinguished by experts. This study aimed to develop a classification model based on deep learning technology to distinguish RGB images of seeds from Ziziphus jujuba Mill. var. spinosa, Ziziphus mauritiana Lam., and Hovenia dulcis Thunb. Using three advanced convolutional neural network (CNN) architectures—ResNet-50, Inception-v3, and DenseNet-121—all models demonstrated a classification performance above 98% on the test set, with classification times as low as 23 ms. These results validate that the models and methods developed in this study can effectively distinguish Z. jujuba seeds from morphologically similar species. Furthermore, the strong performance and speed of these models make them suitable for practical use in quality inspection settings.

The use of summarized spectral data in bands obtained by hyperspectral sensors can make it possible to obtain biochemical information about seeds and, thus, relate the results to seed viability and vigor. Thus, the hypothesis of this work is based on the possibility of obtaining information about the physiological quality of seeds through hyperspectral bands and distinguishing seed lots regarding their quality through wavelengths. The objective was then to evaluate the possibility of differentiating soybean genotypes regarding the physiological quality of seeds using spectral data. The experiment was conducted during the 2021/2022 harvest at the Federal University of Mato Grosso do Sul in a randomized block design with four replicates and 10 F3 soybean populations (G1, G8, G12, G15, G19, G21, G24, G27, G31, and G36). After the maturation of each genotype, seeds were harvested from the central rows of each plot, which consisted of five one-meter rows. Seed samples from each experimental unit were placed in a Petri dish to collect spectral data. Readings were performed in the laboratory at a temperature of 26 °C and using two 60 W halogen lamps as the light source, positioned 15 cm between the sensor and the sample. The sensor used was the Ocean Optics (Florida, USA) model STS-VIS-L-50-400-SMA, which captured the reflectance of the seed sample at wavelengths between 450 and 824 nm. After readings from the hyperspectral sensor, the seeds were subjected to tests for water content, germination, first germination count, electrical conductivity, and tetrazolium. The data obtained were subjected to an analysis of variance and the means were compared by the Scott–Knott test at 5% probability, analyzed using R software version 4.2.3 (Auckland, New Zealand). The data on the physiological quality of the seeds of the soybean genotypes were subjected to principal component analysis (PCA) and associated with the K-means algorithm to form groups according to the similarity and distinction between the genetic materials. After the formation of these groups, spectral curve graphs were constructed for each soybean genotype and for the groups that were formed. The physiological quality of the soybean genotypes can be differentiated using hyperspectral bands. The spectral bands, therefore, provide important information about the physiological quality of soybean seeds. Through the use of hyperspectral sensors and the observation of specific bands, it is possible to differentiate genotypes in terms of seed quality, complementing and/or replacing traditional tests in a fast, accurate, and non-destructive way, reducing the time and investment spent on obtaining information on seed viability and vigor. The results found in this study are promising, and further research is needed in future studies with other species and genotypes. The interval between 450 and 649 nm was the main spectrum band that contributed to the differentiation between soybean genotypes of superior and inferior physiological quality.

Achieving high yields in the large-scale agricultural systems that dominate the US landscape requires critical machine-enabled field operations to be executed in narrow windows of time. Novel cropping systems hold great promise to increase ecosystem services from these large-scale systems, but researchers and end-users need effective methods of representing the timing requirements of such systems. This gap prompted our exploration of approaches to visualize the timing of critical machine-enabled field operations. We refer to the resulting graphic as a field operations visualizer (FOV). We iterated multiple versions of the FOV through a user-centered process involving extensive stakeholder feedback. The resulting FOV version offers a straightforward method of visualizing operation sequences and identifying potential conflicts. Survey results suggest that the FOV provides significant operational insights to users about the timing challenges (or benefits) of novel cropping systems. The FOV may therefore be useful in guiding efforts to improve novel cropping systems and to thereby ultimately increase their deployment to deliver ecosystem services.

Considerable variation in soil often occurs within and across production fields, which can significantly impact farming input management strategies. Optimizing resource utilization while enhancing crop productivity is critical for achieving Sustainable Development Goals (SDGs). This paper proposes a low-cost retrofittable Variable Rate Applicator Controller (VRAC) designed to leverage soil variability and facilitate the adoption of Variable Rate Technologies. The controller operates using a Raspberry Pi platform, RTK—Global Navigation Satellite System (GNSS), a stepper motor, and an anti-slip wheel encoder. The VRAC allows precise, on-the-fly control of the Variable Rate application of farming inputs utilizing an accurate GNSS to pinpoint geographic coordinates in real time. A wheel encoder measures accurate distance travel, providing a real-time calculation of speed with a slip-resistant wheel design for precise RPM readings. The Raspberry Pi platform processes the data, enabling dynamic adjustments of variability based on predefined maps, while the motor driver controls the motor’s RPM. It is designed to be plug-and-play, user-friendly, and accessible for a broader range of farming practices, including seeding rates, dry fertilizer, and liquid fertilizer application. Data logging is performed from various field sensors. The controller exhibits an average of 0.864 s for rate changes from 267 to 45, 45 to 241, 241 to 128, 128 to 218, and 218 to 160 kg/ha at speeds of 8, 11, 16, 19, 24, and 32 km/h. It has an average coefficient of variation of 4.59, an accuracy of 97.17%, a root means square error (RMSE) of 4.57, an R square of 0.994, and an average standard deviation of 1.76 kg for seeding discharge. The cost-effectiveness and retrofitability of this technology offer an increase in precision agriculture adoption to a broader range of farmers and promote sustainable farming practices.

Rice is a water-intensive crop, conventionally grown under submerged conditions, with standing water for about 80% of its growth period. There is an urgent need for water-saving technologies to address challenges associated with conventional irrigation techniques for rice. The alternate wetting and drying (AWD) technique is one of these water-saving techniques; however, it requires continuous monitoring of water levels in the field. The implementation of real-time, electronic sensor-based precision irrigation technology may address the problems associated with conventional irrigation systems and AWD leading to high water use efficiency. Therefore, a study was undertaken to develop a suitable sensor-based automated irrigation system to maintain optimal water levels in rice fields. This study conceptualized an electronic sensor-based automated irrigation system for rice cultivated under the AWD technique. In this method, the rice field is initially flooded to a maximum depth of 5 cm. Irrigation is reapplied once the water level reduces to 10 cm below the soil surface. This developed system helps address water scarcity by regulating water levels, preventing excess ponding. It uses magnetic float-based sensors and electronic circuits to detect water levels, converting them into electronic signals transmitted wirelessly via radio frequency (RF) to a controller. The controller has been programmed for different growth stages that need to be set manually during the cropping period. The system is designed primarily for the AWD method but includes an option for continuous ponding (CP), needed during the flowering stage. The maximum water level at full maturity is set at 5 cm above the soil surface, while irrigation with the AWD method begins when the water level falls 10 cm below the soil surface. The developed system was tested during the Kharif season of 2018–19; the irrigation water productivity was 6.15 kg ha−1mm−1 with the automated system, compared to 3.06 kg ha−1mm−1 in the control (continuous ponding). Total water productivity was 4.80 kg ha−1mm−1 for the automated system and 2.63 kg ha−1mm−1 for the control. The automated system achieved 36% more water savings over the control, which used continuous ponding as farmers practice. The developed system supports AWD, a proven water-saving technique in rice cultivation.

Several years have passed since the linear economy model proved unsustainable, leading to the transition toward the circular economy (CE) model. Significant amounts of agricultural residues and waste from livestock farming units remain unutilized in fields. The anaerobic digestion (AD) method addresses this issue by generating energy in the form of thermal (TE) and electrical energy (EE). This article examines greenhouse heating using thermal energy from a biogas plant. For this purpose, a thermal load model is developed and applied in two regions, northern (Florina) and central Greece (Trikala), to assess the greenhouse’s energy requirements in areas with differing characteristics, especially during the winter months. Additionally, the economic benefits of a biogas plant from selling electricity to the grid are analyzed. Thermal energy constitutes 59.7% of the system’s total energy output. On average, the generated electrical energy amounts to 518 MW h per month, while thermal energy reaches 770 MW h per month. The biogas plant’s daily electricity consumption ranges from 1564 kW h to 2173 kW h, depending on its needs. Ambient temperatures vary between 0 °C and 37 °C, significantly influencing the greenhouse heating system’s efficiency. The biogas plant also demonstrates financial profitability, earning 504,549 € annually from the sale of surplus electricity. Furthermore, the article explores greenhouse crops in the broader Thessaly region, where tomato cultivation seems to be dominant. Greenhouse heating requirements depend on crop type, location, weather conditions, sunlight exposure, and heat loss based on covering materials. Meanwhile, the thermal energy output that can heat a given greenhouse area is directly proportional to the biogas plant’s capacity.

This paper presents a banana ripening chamber system for Romania. The system comprises two main parts: the refrigerating unit, with a cooling capacity of 47.5 kW, and a fresh air supply system for ethylene exhaust during the ripening process (1000 m3/h). The proposed solution replaces the classical one-stage vapor compression with R134a. The new solution presented in this study has a proven fruit ripening solution that includes the 3Es; it is eco-friendly (low GWP refrigerant R1234ze(E)), economical, and energy efficient (AHU with heat recovery). The advantage of the new system results from an increasing coefficient of performance, with 7.34% owing to decreasing the power consumption of the compressors. Regarding heat recovery, the annual energy consumption for ventilation is lower, using (annual average) 41% less energy than without heat recovery.

Currently, intelligent pest monitoring systems transmit entire monitoring images to cloud servers for analysis. This approach not only consumes significant bandwidth and increases monitoring costs, but also struggles with accurately recognizing small-target and overlapping pests. To overcome these challenges, this paper introduces a two-stage multi-feature fusion small-target pest detection algorithm based on edge computing devices, termed TFEMRNet. The algorithm initially conducts semantic segmentation on an edge processor, followed by uploading the segmented images to a cloud server for target identification. Specifically, the semantic segmentation model TFENet incorporates a Multi-Attention Channel Aggregation (MACA) module, which integrates semantic features from EfficientNet-Pest and Swin Transformer, thereby enhancing the model’s ability to extract features of small-target pests. Experimental results demonstrate that TFEMRNet surpasses models such as YOLOv11, Fast R-CNN, and Mask R-CNN on small-target pest datasets, achieving precision of 96.75%, recall of 96.45%, and an F1 score of 95.60%. Notably, the TFENet model within TFEMRNet attains an IoU of 91.63% in semantic segmentation accuracy, outperforming other segmentation models such as U-Net and PSPNet. These findings affirm TFEMRNet’s superior efficacy in small-target pest detection, offering an effective solution for agricultural pest monitoring.

Stocking density (SD) may affect broiler productivity and welfare. This study investigated the performance and welfare of Ross 708 and Cobb 700 broilers as affected by four SDs (27, 29, 32, and 44 kg/m2) until day 56. A total of 432 birds per strain were used, with 10, 12, 14, and 18 birds per pen (1.1 m × 1.5 m), corresponding to the respective SDs. Each SD treatment comprised eight replicates. The target SD was determined based on the projected market weight of 4 kg at 56 days of age. The average body weight (BW), feed intake, and feed conversion ratio (FCR) were measured biweekly. Welfare indicators (four broilers per pen), including gait score, feather cleanliness, feather coverage, body temperature, and footpad condition, were evaluated on days 28 and 56. Tibia strength (two broilers per pen) was measured on day 56. The results show that the BW and FCR of both broiler strains were not affected by SD. For both strains, the male broilers exhibited greater bone strength compared to that of the female broilers (129.06 lbf M vs. 91.70 lbf F for Ross, and 130.86 lbf M vs. 117.40 lbf F for Cobb), but the influence of SD on bone strength was found to be significant only for the Ross male broilers. Most welfare indicators were not affected by the SD on days 28 and 56 for either broiler strain, except for feather cleanliness in Ross broilers and footpad in Cobb broilers on day 56, which improved at lower SDs. Strong age and sex effects on the welfare indicators were also identified for both broiler strains. It was concluded that the SD is not a significant factor for broiler productivity, and it has a minor influence on broiler welfare compared to those of age and sex.

In precision agriculture (PA), monitoring individual plant health is crucial for optimizing yields and minimizing resources. The normalized difference vegetation index (NDVI), a widely used health indicator, typically relies on expensive multispectral cameras. This study introduces a method for predicting the NDVI of blueberry plants using RGB images and deep learning, offering a cost-effective alternative. To identify individual plant bushes, K-means and Gaussian Mixture Model (GMM) clustering were applied. RGB images were transformed into the HSL (hue, saturation, lightness) color space, and the hue channel was constrained using percentiles to exclude extreme values while preserving relevant plant hues. Further refinement was achieved through adaptive pixel-to-pixel distance filtering combined with the Davies–Bouldin Index (DBI) to eliminate pixels deviating from the compact cluster structure. This enhanced clustering accuracy and enabled precise NDVI calculations. A convolutional neural network (CNN) was trained and tested to predict NDVI-based health indices. The model achieved strong performance with mean squared losses of 0.0074, 0.0044, and 0.0021 for training, validation, and test datasets, respectively. The test dataset also yielded a mean absolute error of 0.0369 and a mean percentage error of 4.5851. These results demonstrate the NDVI prediction method’s potential for cost-effective, real-time plant health assessment, particularly in agrobotics.

Closed hydroponics is an environmentally friendly and economical method for growing crops by circulating a nutrient solution while measuring and supplementing various ions contained in the solution. However, conventional monitoring systems in hydroponics do not measure individual ions in the nutrient solution; instead, they predict the total ion content from the pH and electrical conductivity (EC). This method cannot be used to supplement individual ions and adjusts the concentration of the circulating nutrient solution by diluting or supplying a premixed nutrient solution. A more advanced system should be able to identify the concentration of each ion in the nutrient solution and supplement any deficient ions, thus requiring individual ion monitoring systems. Therefore, we first investigated the nitrate, ammonium, phosphate, and potassium (NPK) ion concentration and pH range commonly used for nutrient solutions. Subsequently, we discuss the latest research trends in electrochemical and optical sensors for measuring NPK ions. We then compare the conventional monitoring system (pH and EC-based) and advanced monitoring systems (individual ion sensors) and discuss the respective research trends. In conclusion, we present the hurdles that researchers must overcome in developing agricultural ion sensors for advanced monitoring systems and propose the minimum specifications for agricultural NPK ion sensors.

Sugarcane mechanized harvesting generates large volumes of data that are used to monitor harvesters’ functionalities. The dynamic interaction of the machine-onboard instrumentation–crop system introduces discrepant and noisy values into the data, requiring outlier detectors to support this complex and empirical decision. This study proposes an automatic filtering technique for sugarcane harvesting data to automate the process. A three-step automated filtering algorithm based on a sliding window was developed and further evaluated with four configurations of the maximum variation factor f and six SW sizes. The performance of the proposed method was assessed by using artificial outliers in the datasets with an outlier magnitude (OM) of ±0.01 to ±1.00. Three case studies with real crop data were presented to demonstrate the effectiveness of the proposed filter in detecting outliers of different magnitudes, compared to filtering by another method in the literature. In each dataset, the proposed filter detected nearly 100% of larger (OM = ±1.00 and ±0.80) and medium (OM = ±0.50) magnitudes’ outliers, and approximately 26% of smaller outliers (OM = ±0.10, ±0.05, and ±0.01). The proposed algorithm preserved wider ranges of data compared to the comparative method and presented equivalent results in the identification of regions with different productive potentials of sugarcane in the field. Therefore, the proposed method retained data that reflect sugarcane yield variability at the row level and it can be used in practical application scenarios to deal with large datasets obtained from sugarcane harvesters.

The Eurasian grapevine (Vitis vinifera L.) is one of the most extensively cultivated horticultural crop worldwide, with significant economic relevance, particularly in wine production. Accurate grapevine variety identification is essential for ensuring product authenticity, quality control, and regulatory compliance. Traditional identification methods have inherent limitations limitations; ampelography is subjective and dependent on skilled experts, while molecular analysis is costly and time-consuming. To address these challenges, recent research has focused on applying deep learning (DL) and machine learning (ML) techniques for grapevine variety identification. This study systematically analyses 37 recent studies that employed DL and ML models for this purpose. The objective is to provide a detailed analysis of classification pipelines, highlighting the strengths and limitations of each approach. Most studies use DL models trained on leaf images captured in controlled environments at distances of up to 1.2 m. However, these studies often fail to address practical challenges, such as the inclusion of a broader range of grapevine varieties, using data directly acquired in the vineyards, and the evaluation of models under adverse conditions. This review also suggests potential directions for advancing research in this field.

This study investigates Miscanthus biochar’s potential to reduce ammonia (NH3) emissions in poultry production. Biochar from lignocellulosic biomass has proven a versatile tool in environmental remediation for water, soil, and air quality applications with ample opportunity for inclusion in agricultural systems. Ammonia emissions present a concern for animal/human health and the environment. The impacts of biochar production temperature (400 and 700 °C), organic acid activation (acetic acid, citric acid), and application rate (0.24 and 0.49 kg m−2) on broiler litter NH3 emissions were evaluated. Biochar production parameters, i.e., temperature, and acid type were found to significantly impact its performance as an NH3 control measure. The following factors, ranked by magnitude of impact, were found to statistically impact the NH3 emission rate: biochar application rate (p < 0.001), biochar production temperature (p = 0.003), and lastly acid type (p = 0.007). The best performing biochar was produced at 400 °C, activated with acetic acid, and applied at a high addition rate (0.49 kg m−2). This treatment reduced cumulative NH3 volatilization after 2 weeks by 19.7%.

Deep learning-based methods have proven to be effective for various purposes in the agricultural sector. However, these methods require large amounts of labelled data, which are difficult to prepare and preprocess. To overcome this problem, we propose the use of a latent diffusion model for plant disease detection by generating unseen class images. In this study, we used images of healthy and diseased grape leaves as training datasets and utilized the latent diffusion model, known for its superior performance in image generation, to generate images of diseased apple leaves that were not included in this dataset. Image-to-image generation was utilized to preserve the original healthy leaf features, which enabled the appropriate image generation of diseased apple leaves. To ascertain whether the generated diseased apple leaf images could be used to detect leaf diseases, a deep learning-based classification model was trained to discriminate between diseased and healthy apple leaves from a dataset with a mixture of actual and generated images. Results showed that leaves were accurately classified, indicating that diseased apple leaves not included in the training data could be used to identify the actual diseased apple leaves. Our approach opens up new avenues for improving plant disease detection methods.

Coffee is a global commodity, with Brazil being a major producer, particularly in the Minas Gerais state. This study applied machine learning to predict the Arabica coffee yield in the region, analyzing two groups of cultivars (G1 and G2) using data from 1993 to 2020. The Factor Analysis of Mixed Data (FAMD) was employed to explore the relationships between climatic factors, management practices, and the coffee yield. Four machine learning models, such as Multiple Linear Regression (MLR), Random Forest (RF), XGBoost (XGB), and Support Vector Machines (SVM) were calibrated and evaluated for yield prediction. The FAMD revealed complex interactions among variables, requiring four principal components to explain approximately 64.6% of the total variance. Management practices, such as the planting density and pruning, had a stronger influence on G1 cultivars, while G2 cultivars were more sensitive to climatic conditions, particularly the air temperature. Among the machine learning models, RF and XGB performed best in the yield estimation, whereas MLR and SVM were less effective, particularly for values above 60 bags ha−1 (1 bag = 60 kg). These findings underscore the variability in the yield across cultivars and demonstrate the potential of machine learning to guide tailored management strategies for different coffee cultivars.

Excimer fluorescent ultraviolet (UV) lamps (UV–228) function as mercury–free sources that use excimer emissions as excitation light sources. First, we evaluated the effects of UV–228–based irradiation (λmax = 228 nm) on the viability of fungi, using Penicillium digitatum, and compared these effects with those of other light sources, such as OEL–222 (λmax = 222 nm) and GL–6 (λmax = 254 nm), to evaluate the effectiveness of the excimer fluorescent lamp. Next, we investigated the effects of UV–228–based irradiation on strawberry storage. Although UV–228 affected weight loss, Brix, ascorbic acid, polyphenol, and DPPH, the effects of UV irradiation for 5 min on strawberry fruit quality were minimal; i.e., only weight loss and ascorbic acid content were higher than those in the non–irradiated treatment. We found that 5 min of UV–C irradiation using UV–228 prevented mold emergence for up to 11 days of storage in strawberries These results indicate that UV–228 contributes to long–term strawberry storage.

Non-satisfactory bread-making quality in wheat, a Tajik staple, hampers food security in Tajikistan and calls for plant breeding efforts. Here, methods were searched for to study grain protein composition, which is of use for Tajik plant breeding to improve bread-making quality. Size-exclusion high-performance liquid chromatography (SE-HPLC) was used to determine protein composition in 22 wheat varieties and breeding lines grown in two locations, which were then compared with the specific protein composition evaluated using electrophoresis and previous results from Tajik breeding and farmer-grown wheat. As Tajik wheat generally showed a large variation in high-molecular-weight glutenin subunit (HMW-GS) composition, with several allelic variants in the same line, single-seed selection was required when using this methodology in breeding for improved bread-making quality, and such an evaluation willalso result in more homogenous lines for protein composition. SE-HPLC was found to be a suitable tool to evaluate protein composition in the current Tajik wheat material with a heterogeneous protein composition, which might be advantageous for adaptation to the local and future climate. However, more easy-to-handle and high-throughput methods, e.g., marker-assisted selection, could be preferable alternatives for studying protein composition in wheat and for use in breeding for increased bread-making quality to increase food security in Tajikistan.

Salinity is one of the major abiotic stresses that abate the yield of several crop species including rice. Several studies were conducted to identify quantitative trait loci (QTLs) for traits associated with salinity tolerance, mostly at the seedling stage of crop growth. However, the reproductive stage of development is highly sensitive to salt stress, and hence, better QTLs must be developed. QTLs have been identified in the present study for salt tolerance of the reproductive stage in rice using recombinant inbred lines (RILs). Thirty-day-old rice seedlings of 184 RILs derived from a cross between a salt sensitive RP Bio226 (indica), and a salt-tolerant Jarava (indica), were used to identify QTLs linked to salinity tolerance in moderate (field) and severe (pot) stress conditions. One hundred polymorphic simple sequence repeat (SSR) markers were used to construct a genetic linkage map that covered a 1349.4 cm genome with an average distance of 13.5 cm between loci. Eighteen new QTLs [logarithm of odds (LOD) 2.5 and above] were identified on chromosomes 1, 2, 6, 10, 11, and 12 using composite interval mapping with the phenotypic variation explained by QTL (PVE) as high as >42% with an LOD value of 5.2. qYLSt-12 with an LOD of 2.8 and a phenotypic variance (PV) of 6.4%, flanked by RM27940-RM27971, was identified for yield in moderate stress conditions. The qSTR-2 detected for salinity tolerance on chromosome 2 with 8.9% of the PV is the most significant finding of the present research. No QTL for salinity component traits has been reported in the region of RM110-RM423. The other salinity trait QTLs identified are qSN-11, qSN-12 for Na+ concentration with a total PVE% of 13.9 and qSNK-12.1, qSNK-12.2 for the Na+/K+ ratio showing a total of 26.7% of the PV. The QTLs for yield component traits viz. plant height, panicle number, panicle length, and biomass were also identified in the present study. Previous studies reported QTLs for salinity tolerance in rice on chromosome 1 but none of the QTLs in our study were on qSaltol or nearby position; therefore, Jarava conferred salinity tolerance in RILs due to novel QTLs. Fine mapping of these novel QTLs is suggested and could be helpful to enhance the level of tolerance through marker-assisted selection for the pyramiding of different QTLs in one background.

Anthracnose and grain mold are two of the most significant diseases of sorghum, a versatile crop that plays an important part in the daily lives of millions of inhabitants, especially in the drier tropical regions. The aim of this study was to determine the influence of four agronomic traits in selected sorghum germplasms on the two diseases using Spearman’s ρ test to identify significant pairwise correlations. Both anthracnose and grain mold scores were significantly and negatively correlated with seed weight and germination rate. The grain mold infection score also demonstrated negative correlations with plant height (Spearman ρ = −0.61 and p-value = p-value = 0.0022). In this investigation, principal component analysis and clustering variables analysis revealed that seed weight and germination rate exhibited a directional alignment, suggesting a positive association. Similarly, panicle length and plant height clustered together, suggesting a shared variation pattern. Additionally, a support vector machine and random forest models effectively predicted the germination rate based on the studied traits, highlighting the potential of machine learning in understanding complex trait relationships in sorghum. This work provides insights into the relationship between agronomic traits and disease resistance, thus contributing to sorghum improvement efforts.

The present study investigates the cryopreservation of embryonic axes from the peanut genotype ECU-12466, demonstrating a successful protocol involving a 1 h desiccation on silica gel followed by a 1 h cryoprotection with Plant Vitrificatin Solution 2 (PVS2). The average dimensions of the excised embryonic axes were 5.6 mm in length and 3.5 mm in width, with plumule lengths averaging 2.2 mm. Notably, germination rates for cryopreserved axes ranged from 71.4% to 85.7%, showing resilience to varying desiccation and PVS2 treatment times, particularly at 1 h. Shoot length was significantly enhanced by a 1 h PVS2 exposure, while longer durations resulted in phytotoxic effects. Rooting rates were higher for samples treated with shorter desiccation periods, with 54% rooting achieved at 1 h of PVS2 exposure, contrasting sharply with just 16% at 2 h. The moisture content of the embryonic axes remained stable between 9.3% and 9.5%, indicating no detrimental impact from the applied treatments. To evaluate the protocol’s broader applicability, five additional peanut genotypes (ECU-11401, ECU-11418, ECU-11448, ECU-11469, and ECU-11494) were tested. While cryopreserved samples demonstrated high germination rates of up to 95.4% after 15 days, the rooting success was significantly lower (25.2%) compared to the control group’s 90.9%. Following transplantation, the growth performance varied among genotypes, with a success rate of 93% for ECU-11494. Overall, this study elucidates the critical parameters for optimizing cryopreservation protocols in peanut embryonic axes, contributing to more effective long-term conservation strategies.

The composition of macro- and microbiota in soils formed in treatment facilities (filtration fields) of the sugar industry in the Chernozem zone of Russia was studied. Soil moisture and the abundance and diversity of organic and mineral substances entering treated plants with wastewater have a significant impact on communities of microorganisms and invertebrates. The macrofauna of the soils of periodically operating settling ponds (called checks, or carts) consists mainly of predators and phytophages; the soil of the checks that was decommissioned 45 years ago consists of saprophages, which were dominated by earthworms. All groups of macrofauna in the soils of filtration fields are characteristic of the soils of forest-steppe regions, while their numbers are significantly lower compared to the Chernozems under the oak groves of the Central Chernozem Reserve, but noticeably higher than in the surrounding arable Chernozems. The microflora in the soils of the filtration fields and the background Haplic Chernozem (Pachic) is identical in the composition of physiological groups, but differs in their number and distribution along the soil profile. Differences in the diversity and distribution of microorganisms in the studied soils depend on different water regimes, determined by the frequency and duration of exposure to wastewater and the input of sugar production waste enriched with calcium carbonate, organic matter and biophilic elements.

The quality of arable soils largely determines the productivity of agricultural crops and the economic efficiency of their cultivation. The purpose of this work is to analyze the relationship between intra-field heterogeneity of crops and vertical heterogeneity of soil skeletality based on a test corn field in Kabardino-Balkaria. The NDVI vegetation index, calculated from Sentinel-2 satellite imagery, was used as an indicator of crop condition. Soil skeletality was determined by dry sieving. It has been established that the value of the NDVI index of crops on a test field can serve as the basis for indirect detection of vertical heterogeneity of agronomically important soil properties. To obtain reliable information, accurate timing of obtaining NDVI values and the crop cultivated in the field is necessary. The indicator reliability of different crops is determined by their phenology and the thickness of the potential root layer. The geography of agronomically important soil properties on the test field does not correlate with the areas of the traditionally compiled soil map. Spatial variation in corn yield on the test field (range 23%) correlates with variation in the soil skeletal profile. To use NDVI values as an indicator of spatial variation in agronomically important soil properties, it is necessary to take into account the type of crop cultivated, careful selection of the date for obtaining NDVI, as well as the availability of a priori expert knowledge about the limiting soil factors in the field, the specific phenology and agrotechnology of crop cultivation.

The soil cover of cities and towns within their administrative boundaries is composed of soils, more or less modified by the urban environment along with native, agricultural soils and non-soils. For preserving this diversity when introducing these new objects to the unified digital model of the soil cover of Russia, soil maps of 10 “test” cities have been compiled. The cities are located in several natural zones, and are intermediate in terms of their population. Mapping was performed by means of detailed purposeful deciphering, data on mapping units on the original soil map, and recent systematics of urban soils and their dependence on the urban environment. The approaches applied were supported by the experience in soil mapping of St-Petersburg, Moscow, Volgograd and few other cities. Soils were qualified in the system of Russian soil classification, and their areas, along with those of non-soils, were regarded as units in the theory of soil cover patterns. In all cities investigated, soil associations comprised urban soils per se, transitional soils including agro-soils and conventionally native ones; their ratios in the soil cover depend on both historical-socio-economical reasons and the natural enviroment.

Digital soil rheometry is a relatively new and rapidly developing research direction that currently occupies a small niche in soil and agrophysics studies. This paper presents a review of studies based on the rheometry approach as part of soil mechanics research in different countries. The fundamental principles and equations of oscillatory amplitude sweep tests and oscillatory tests with controlled strain (resilience test) are described, with a focus on soil structure as the main object of the study. Key deformation and strength parameters (storage and loss moduli (G' and G``), the linear viscoelasticity range (LVE-range), shear stress (t), structural recovery index (R), etc.) describing the mechanical behavior of the soil at the microstructural level are discussed. This paper also highlights the influence of soil preparation: soil structure, water content, temperature, and the normal force applied during tests as parts of the research protocol – on the obtained results. Furthermore, this review examines research trends within the rheometry approach that have emerged over the past two decades of its application to soil studies. Based on the analysis of existing studies, potential directions for the further development of rheometry in soil research are proposed.

Different laboratory methods of soil quality assessment are considered by the example of soils of the North-Pre-Caucasian province. The article presents a detailed protocol for quantitative determination of Permanganate oxidizable carbon (POXC) fraction using 0.2 M potassium permanganate solution. The data are presented for chernozem type soils of Rostov region belonging to different agroecological groups. Samples of ordinary chernozems (Haplic Chernozems) and meadow-chernozems (Gleyic Chernozems) soils of agrolandscapes were used. Data from non-tilled plots were used to estimate background parameters. Correlation POXC with fraction of bichromate-oxidizable carbon, light fraction (LF< 1.6 g/cm3 ) and waterhydrolysable fractions were estimated. It was found that the content of POXC fraction varied in the range of 126–1 006 mg/kg, with maximum values, on average 925 mg/kg, in samples of humus horizons of ordinary chernozems of plakorny agroecological group of untreated plots. The minimum content of POXC fraction was characteristic for samples of AB horizons of arable plakorny and arable low- and medium eroded agroecological soil groups, on average 218 and 137 mg/kg, respectively. POXC has significant correlations with the fraction of bichromate-oxidizable carbon, the content of light fraction, the content of total nitrogen and soil density, and has shown to be a good marker of agro-ecological conditions on the studied set of samples.

Information about land use history is needed for investigation of soil cover transformation, evolution and degradation under human economic activity. The aim of article is to submit results of retrospective analysis of agricultural land use at the zone of light-chestnut solonetzic soil cover. Object is lands of the Experimental station “Oroshaemaya”, Volgograd region, Volga-Don irrigation system. Analysis is carried out on the bases of many space images, archive data and consultations of specialists from All-Russian Research Institute of Irrigated Agriculture. Schemes of land use at different time periods from 1970s till 2023, diagrams of dynamics of dry arable lands, irrigated lands and agricultural fallow are submitted. Nine variants of changing land areas over time are identified: 1 – dry arable land constantly from 1970; 2 – dry arable land → irrigated land → dry arable land; 3 – irrigated land → dry arable land; 4 – dry arable land → irrigated land; 5 – irrigated land → dry arable land → irrigated land; 6 – irrigated land → dry arable land → agricultural fallow; 7 – dry arable land → agricultural fallow; 8 – agricultural fallow constantly from 1970; 9 – unused lands in recent years. Duration of the same type of use for each field is determined. Statistical characteristics of plow horizon thickness in soils of Experimental station are calculated.

The analysis of the practice for regulating the use of dumps of soils and subsoils in the Russian Federation is carried out. It is shown that the existing regulations do not meet modern requirements for the protection and rational use of dumps of soils and subsoils. The absence of a unified recycling approach has been revealed. In some regulatory documents, dumps of soils and subsoils are considered as material for the reclamation of disturbed lands, in others – as industrial waste. Disagreement with the practice of classifying all dumps of soils and subsoils as waste of hazard classes 3–5 and subject to export to special polygons is justified. Disposal of such waste materials is administratively or criminally punishable. This approach to the material of soil excavation causes great harm to both the economy and the environment as a whole. It is proposed to change the practice of irrational use of soil and subsoil dump material and make appropriate changes to regulatory documents. One of such changes may be the introduction of the gradation “environmentally safe waste” into the documents on the definition of the hazard class of waste, with the prescription for the use of soil and subsoil dumps belonging to this category without restrictions on use in the national economy. It is proposed to clarify the magnitude of the soil thickness (soil layer) of 5 m, including the maximum depth of occurrence of fertile soil layer and potentially fertile layer. This value corresponds to the accepted stratigraphic rule on the general regularity of the structure of the sedimentary shell of the Earth. According to this point of view, soils are underlain by subsurface geological rocks, which does not imply the presence of intermediate geological layers between soils and subsurface.

The study was carried out in blueberry pine forests of the middle taiga subzone of Karelia. The results of complex studies of the microbiocenosis of Al-Fe-humus soils (Albic Podzols) formed on fluvioglacial deposits are presented. The methods of luminescent microscopy and substrateinduced respiration were used to assess the carbon of microbial biomass (Cmic). To study the microbiological activity of soils in relation to the mineralization of soil organic matter, the basal respiration method was used, and the metabolic coefficient was calculated. It is shown that the changes in the studied indicators reflect the natural and climatic conditions and the specifics of the soil of Al-Fe-humus genesis. The highest values of Cmic and basal respiration were observed in the subhorizons of the forest floor, and the lowest in the eluvial and illuvial horizons. The results obtained can be used as microbiological indicators in modeling carbon cycling processes in Al-Fehumus soils of forest ecosystems.

The purpose of the work was to study the concentration of various forms of mineral nitrogen and nitrogen cycle enzymes in farmland soils. The objectives of the research included studying the content of ammonium and nitrate forms of nitrogen, as well as the activity of urease and nitrate reductase in soils of farmland on the territory of the Donetsk People's Republic (DPR). To study the soils of agricultural lands, model sites located in the southern part of the Shakhtersky district of the DPR were selected and represented by moderately washed-out chernozems with low, low and medium humus. The control was an area with steppe vegetation (ordinary chernozem, thick, with medium humus content). The results of the researches allow us to draw conclusions about the development of degradation processes in a number of model sites and their impact on the content of ammonium and nitrate nitrogen in agricultural soils. Growing crops removing substantial amount of mineral nutrients, the slope of the sites surface, and the duration of cultivation of grain crops in the same sites lead to a significant decrease in the concentration of ammonium and nitrate nitrogen. Thus, the most significant decrease in the concentration of nitrogen of ammonium compounds (by 77–82%) was recorded in model slope areas under maize and sunflower. Analysis of the data showed that exchange ammonium is confined to the underlying genetic horizons. The minimum concentration of nitrates in the arable horizon was recorded under the joint influence of such unfavorable factors as: unreasonable layout of fields along the relief elements; violation of crop rotation conditions; use of precursor crops with a significant level of removal of nutrients (wheat, maize). When studying the activity of urease, it was found that a low number of soil microorganisms associated with the cultivation of maize and sunflower, as well as with the development of degradation processes of various genesis, led to the suppression of the functioning of the urease of soils in model areas. The maximum urease activity was observed in areas under winter wheat and amounted to 68–72% in relation to the control indicators. As a result of studies of nitrate reductase activity, a decrease in its values was also found in areas used to grow crops that form a large phytomass (maize and sunflower).

The results of research on the example of southern chernozem and chestnut soil are presented. Long-term use of plowing with rotation of the soil layer and the use of fallows leads to a critical state of the soil in terms of the content of aggregates measuring >10 mm and >1 mm. The introduction of notill on old arable soils leads to a general increase in the mean weighted diameter of aggregates (MWD). The intensity of restoration of the structural state of the soil during no-till in southern chernozem occurs more intensively than in chestnut soil, and the main changes are recorded in the fractions of agronomically valuable aggregates. In southern chernozem there is an increase in diameter from 6.0 mm to 9.5 mm, while in chestnut soil the MWD increases only to 7 mm. The content of erosion-susceptible aggregates

【Objective】 Plants in most urban areas need irrigation, and understanding their demand for irrigation is important for urban water management. This paper analyzes the spatiotemporal variation in irrigation demand of green spaces in small and medium-sized metropolitan cities in arid areas of Northwest China, as well as the influencing factors. 【Method】 A net irrigation water accounting model was developed for plot scale by integrating meteorological data, root zone soil moisture, surface quantitative remote sensing data, and the principle of root zone soil water balance. Quantile random forest and Bayesian linear regression models were used to identify key factors that influence irrigation water demand. 【Result】 ① In 2022, the daily average net irrigation water demand of green space plots estimated from the model ranged from 0.45 to 0.85 mm, slightly lower than the measured values. Temporarily, irrigation demand peaked during the vegetation growth period. Spatially, plots with denser, healthier vegetation had high demand for irrigation, showing significant spatial autocorrelation. ② Key factors that affect irrigation demand included root zone soil moisture, surface temperature, vegetation growth, sunshine duration and air temperature. Meteorological data and surface remote sensing images explained 61% and 78% of the variation in net irrigation water demand, respectively, highlighting the multifaceted nature of irrigation demand. 【Conclusion】 Understanding the demand of urban green spaces for irrigation at plot scale has an important implication for urban water resource allocation and improving urban water management.

【Objective】 The effect of temperature on plant development is often characterized by degree days or accumulated temperature. In this paper, we investigate how planting date affects accumulated temperature and the consequence for the yield of two ratooning rice varieties. 【Method】 The experiment was conducted in a rice field in Changsha, central Hunan province. It included two rice varieties: Y Liangyou 900 (YLY900), which has a long growth period, and Y Liangyou 911 (YLY911), which has a moderate growth period. The seeds of both varieties were sown on March 15 (B1), March 20 (B2), March 25 (B3), March 30 (B4), April 5 (B5), or April 10 (B6) to establish a planting date gradient. In each treatment, we measured physiological parameters including growth duration, yield component, and seed-setting rate to evaluate the physiological development of the crops. 【Result】 The average annual growth period of YLY911 was 215 days. Its first and regeneration seasons were eight and ten days shorter, respectively, than those of YLY900. Delaying the planting date shortened the vegetative growth period of the first season and reduced the total growth period of both seasons in both varieties. The variation in grain yield in the first and regeneration season, as well as annual yield of YLY911, followed a parabolic relationship, peaking in B2. Conversely, the yield of YLY900 decreased steadily as planting date was delayed. Yields in the first and regeneration season, as well as annual yield, were consistently higher in YLY911 than in YLY900. The primary factors affecting grain yields were the number of ears per unit area and the seed-setting rate. With delay in planting dates, the annual accumulated temperature for both varieties first increased and then declined, peaking in B2. The accumulated temperature in the first and annual seasons for YLY900 was significantly higher than for YLY911, while the accumulated temperature in the regeneration season was lower for YLY900 than for YLY911. Such differences in the regeneration season between the two varieties were statistically insignificant (p>0.05). 【Conclusion】 For ratooning rice in our studied area, the optimal planting date was March 15 for varieties with long growth period and March 20 varieties with moderate growth period.

【Objective】 Microplastics have emerged as a significant contaminant over the past few decades in various ecosystems. While their presence and impacts in other ecosystems have been well documented, their occurrence in soils and potential implications for environmental and ecological functions, as well as food security, remain poorly understood. This paper reviews the occurrence of microplastics in farmlands in China. 【Method】 The review is based on publications indexed by the Web of Science and CNKI databases. The search results were analyzed using the VOSviewer software, and the microplastics studied in these papers were characterized by their types, abundance, particle diameter, color, and geometric shapes. 【Result】 The types of microplastics found in farmland soils in China show both similarities and regional variations. Polyethylene and polypropylene, mainly from agricultural use and plastic waste, are the dominant microplastics found across most provinces. Microplastics are most abundant in farmland soils in Inner Mongolia, Xinjiang, Shandong, and Guangxi, with their content exceeding 10 000 particles per kilogram of soil. The diameter of microplastic particles primarily ranges from 0 to 2 mm but varies regionally. For example, in Xinjiang and Guizhou, approximately 85% of microplastic particles have a diameter in the range of 0-0.5 mm and 0-0.1 mm, respectively. Common microplastic colors include transparent, white, black, blue, and yellow, with predominant shapes being fibrous, film, fragment, and foam. Based on these findings, this study proposes strategies for mitigating soil microplastic pollution. 【Conclusion】 This review provides valuable insights into the occurrence and characteristics of microplastic pollution in farmland soils in China. These findings provide guidance for developing policies and practices to prevent and control soil microplastic pollution to ensure sustainability of agricultural production and environmental health.

【Objective】 To study the effects of applying different amounts of biochar on the growth and yield of rice in the Jiansanjiang area under flooded irrigation and alternating wet and dry irrigation modes, and to determine the appropriate amount of biochar under different irrigation modes for rice in this area. 【Method】 The experiment was carried out as a field trial using two irrigation modes: flood irrigation (CF) and alternating wet and dry irrigation (AWD). Additionally, three biochar application rates were used: B5 (5 t/hm2), B15 (15 t/hm2), and B25 (25 t/hm2). Flooded irrigation without biochar treatment was used as the control (CK) to study the effects of different biochar application rates on rice growth and yield under two irrigation modes. 【Result】 The application of biochar promoted plant tillering and plant height growth under both irrigation models, but the difference with CK was not significant. Biochar application favored rice LAI, dry matter and yield formation in the Jiansanjiang area, with the most pronounced effect in the B15 treatment. The LAI, dry matter mass and yield of rice in B15 treatment under CF irrigation mode were significantly increased by 39.8%, 17.0% and 10.0%, respectively, compared with those in CK. The LAI, dry matter mass, and yield of rice in B15 treatment under AWD irrigation model were significantly increased by 36.9%, 18.3%, and 11.3%, respectively, compared with those in CK. Irrigation modes had less effect on tillering and plant height of rice under the same biochar application rate, but AWD irrigation model was more favorable for dry matter mass and yield. Mean rice dry matter mass and yield increased by 1.0% and 0.8%, respectively, under the AWD irrigation mode compared to the CF irrigation mode. Water consumption of rice under CF irrigation mode was similar to that of CK, whereas water consumption of rice under AWD irrigation mode was 11.0% less than that of CK. Water use efficiency in AWD irrigation mode increased by 13.3% than that of CF mode. Under the same irrigation mode, rice water use efficiency was significantly higher in the B15 treatment than in the other treatments. Water use efficiency of B15 treatment was 9.7% and 25.5% higher than that of CK under CF and AWD irrigation mode, respectively. 【Conclusion】 The application of biochar in the Jiansanjiang area can promote the growth and yield formation of rice, and the increase in yield firstly increases with the increase of biochar application rate and then decreases, taking into account the increase in yield, water saving and other factors, the 15 t/hm2 biochar application rate in the AWD mode is the best water-charcoal coupling mode.

【Objective】 Hetao Irrigation District is one of the largest irrigation districts in Northwestern China. However, the imbalance between irrigation and drainage has resulted in soil salinization. The purpose of this paper is to investigate the characteristics of soil salinization in both planted and uncultivated soils in this region, as well as the key environmental factors that influence soil salinization. 【Method】 Field investigation and laboratory test were conducted to analyze the ion characteristics and its influential factors in cultivated and uncultivated soils. 【Result】 In the studied area, the predominant anion in both cultivated and uncultivated soils was chloride (Cl-), comprising 59.31% and 58.48% of the composition, respectively. The primary cations were potassium (K+) and sodium (Na+), accounting for 71.32% in cultivated soils and 72.64% in uncultivated soils. Correlation analysis showed that, in cultivated soils, K+ and Na+ were most correlated with sulfate (SO42-) with a correlation coefficient of 0.87, followed by with Cl-, with a correlation coefficient of 0.66; they were least correlated with HCO3-. In the uncultivated soils, K+ and Na+ were mostly correlated with Cl-, with a correlation coefficient of 0.93, followed by with SO42-, with a correlation coefficient of 0.73. Key factors affecting salinization in the cultivated soils included SO42-, magnesium (Mg2+), Cl-, K+ and Na+, while in the uncultivated soil, they were SO42-, Cl-, K+, Na+ and HCO3-. Redundancy analysis showed that salt ions and environmental factors accounted for 13.56% and 20.23% of the variation in salinization in cultivated soils, and 35.17% and 46.17% in salinization in cultivated soils, respectively. Groundwater quality affected soil salt ions the most, followed by soil texture. 【Conclusion】 Soil salinization in both cultivated and uncultivated soils is severe, with Cl-, K+, Na+ and SO42- being the dominant ions. Effective strategies to mitigate soil salinization should focus on improving groundwater quality and optimizing soil texture.

【Objective】 Frequent droughts have significantly impacted agricultural productivity and ecological health of the Loess Plateau. This study investigates the characteristics and driving factors of dry-wet evolution in the region. 【Method】 The analysis was based on meteorological data, including temperature, precipitation, relative humidity, sunshine hours, and wind speed, measured from 1961 to 2020 at 71 weather stations across the Loess Plateau. The changes in wet and dry spells and their underlying determinants were analyzed using standardized precipitation evapotranspiration index (SPEI), Mann Kendall test, and run length theory. 【Result】 Temporally, temperature showed a significant increase, while wind speed, relative humidity and sunshine hours showed significant decreases from 1961 to 2020. In contrast, precipitation showed a weak decrease, while ET0 (reference crop evapotranspiration) exhibited a first increase followed by a decrease during this period. Spatially, the Northwest saw increased precipitation and decreased ET0. Conversely, the Northeast experienced declines in both precipitation and ET0. From 1999 onward, the SPEI showed an initial decrease followed by an increase. The Southeast experienced high drought frequency but with shorter durations and lower severity, extreme drought events in which were rare. In contrast, the Northwest had fewer but more severe and prolonged droughts. Rising temperatures, increasing precipitation and decreasing ET0 were the primary drivers of dry-wet alterations. ET0 had a stronger influence on dryness and wetness patterns than precipitation. The correlation between the mid-latitude westerly belt, sunspot activity, and SPEI was weak, indicating limited influence of large-scale atmospheric and solar factors. 【Conclusion】 The Loess Plateau showed a warm-drying trend from 1961 to 1999, followed by a warm-wetting trend thereafter. These findings improve our understanding of hydrological changes and help develop mitigating strategies for managing drought and water resources in the region.

【Objective】 Evaluating evapotranspiration and soil salinization is critical for soil and water management but challenging on large scales. This paper investigates the feasibility of using remote sensing technologies and meteorological data to estimate evapotranspiration and soil salinization in irrigated areas. 【Method】 The study was conducted in the Manasi River Basin in Northeastern China. It was based on the Surface Energy Balance System (SEBS) model, using the Landsat-8 remote sensing data and field sampling to estimate spatial distribution of both evapotranspiration and soil salinity in the studied area. 【Result】 ① The normalized vegetation index (R2 = 0.644 8), surface specific emissivity (R2 = 0.637 7), and surface temperature (R2 = 0.558 3) all showed a strong correlation with soil salinity, while surface albedo (R2 = 0.198 6) had a weaker correlation with soil salinity. ② Daily evapotranspiration (ET) in the Manasi River Basin ranged from 0.024 to 5.403 mm, with an average of 3.866 mm. ET decreased from the Southern irrigation area to the Northern Gobi region. ③ ET values less than 3.60 mm/d were associated with non-saline and moderate saline soils. For areas with ET between 3.60 mm/d and 4.05 mm/d, moderate soil salinity dominated. For areas with ET exceeding 4.05 mm/d, the proportion of severe soil salinity increased significantly. 【Conclusion】 Using remotely sensed satellite imagery, the SEBS model provides an accurate, high-resolution estimate of evapotranspiration in terrestrial systems. The evapotranspiration was positively correlated with soil salinity in the Manasi River Basin. These results can help improve land and water management in the region.

【Objective】 Soil anaerobicity is a common abiotic stress in fine-textured soils that adversely affects crop growth. Aerated irrigation, achieved by adding air bubbles to irrigation water, is a technique to mitigate soil anaerobicity. This study investigates the effect of regulated aerated irrigation on rice growth, physiological traits at different growth stages, as well as grain yield. 【Method】 The experiment was conducted in pots using the Zhongzao 39 rice variety as model plant. The controlled irrigation was implemented by keeping soil moisture at 80%-100% (T1) or 60%-80% (T3) of the field capacity during late-tillering stage; at 80%-100% at late-tillering and booting stage (T2); at 60%-80% during late-tillering stage and 80%-100% during booting stage (T4); at 80%-100% during middle and late tillering stages (T5), at 80%-100% from middle tillering stage to booting stage (T6), at 80%-100% during mid-tillering stage and 60%-80% during late tillering stage (T7), at 80%-100% during mid-tillering and booting stages and 60%-80% during later tillering stage. During non-controlled stages, all treatments kept a thin water layer at the depth of 0-30 cm on the soil surface. The control (CK) was conventional flooding irrigation by keeping a 0-30 cm of water layer on the soil surface. For each treatment, we measured abscisic acid (ABA), peroxidase activity (POD), pollen viability, OsFKF1 gene expression and grain yield. 【Result】 Compared with CK, the treatments significantly increased pollen viability, OsFKF1 expression at the heading and flowering stages, and grain yield. T3, T2 and T8 achieved the highest pollen viability (83.28%), OsFKF1 expression (2.96), and grain yield (83.908 g/plant), respectively. ABA activity in roots and leaves, and POD activity in leaves, at the mid-tillering stage differed significantly between treatments. Compared with CK, the treatments reduced ABA in roots at the mid-tillering stage by 16.73%-23.69% and POD activity in leaves at the late-tillering stage by 19.91%-66.70%; conversely, they increased ABA in roots at the booting stage by 1.37%-63.52%. At the mid-tillering stage, T1—T4 reduced POD activity in roots by 70.36%-71.55%, whereas T5—T8 increased POD activity in roots by 2.53%-7.00%. Multiple linear regression analysis showed that the treatments increased ABA in roots at the late-tillering stage, reduced ABA in leaves at the late-tillering and booting stages, and decreased POD activity in leaves at the mid-tillering and late-tillering stages. These enhanced pollen viability and OsFKF1 gene expression at the heading and flowering stages, thereby increasing grain yield. 【Conclusion】 Among all treatments, T3 was the most effective in regulating ABA and POD, enhancing pollen viability, and improving rice grain yield, which reached 82.18 g/plant.

Saline-alkali soils in Xinjiang are extensive and diverse. Reclaiming these soils for agricultural production plays a critical role in safeguarding food security in China. Exploring reclamation technologies that align with the arid environmental nature and modern water-saving irrigation techniques unique in the region is crucial for the sustainable use of these saline-alkali soils. This paper reviews the historical development and effectiveness of various saline-alkali soil management techniques in Xinjiang, based on literature review and quantitative evaluation of available data. We analyze the bottlenecks in managing saline-alkali soils under the constraints of limited water resources, providing insight into comprehensive utilization strategies for saline-alkali soils in the context of new challenges faced by the region. Based on the balance between water and soil resources, as well as the characteristics of secondary soil salinization in irrigated farmland, we propose an integrated ‘prevention, control, and treatment’ strategy for soil management in watersheds and irrigation districts in the region. Our recommendations include determining rational water resource allocation for irrigation and ecological maintenance, identifying effective techniques to desalinate specific soil layers in drip-irrigated farmlands, establishing dynamic monitoring systems to monitor soil salinization, and implementing prevention-control measures tailored to different types of salinized soils in different areas in the region. These provide a guidance for policymaking to improve management and utilization of saline-alkali soils in Xinjiang.

【Objective】 Reference crop evapotranspiration (ET0) is a critical parameter for irrigation and water management. This paper proposes a method for real-time forecasting ET0 using weather forecast data and a deep learning approach. 【Method】 The study was conducted in Xiaoshan District, Hangzhou City, Zhejiang Province. Hourly measured weather data and 1-7 day forecasted weather data from April 24, 2021 to December 31, 2023 were used as the dataset. The forecasting accuracy of the weather data was analyzed. A deep learning model based on the backpropagation (BP) neural network algorithm was developed and deployed for online training using Alibaba Cloud servers. 【Result】 The accuracy of the input parameters was generally reliable, with minimum temperature forecasts being more accurate than maximum temperature forecasts. Forecasting accuracy decreased as the lead time increased. Errors were observed in forecasting weather types and wind scales. The ET0 predicted by the model closely matched those calculated using real-time data, demonstrating high forecasting accuracy. During the training period, the model achieved a maximum accuracy of 91.56%, with an average root mean square error (RMSE) of 0.828 mm/day and a mean absolute error (MAE) of 0.667 mm/day. During the testing period, the model achieved an accuracy of 84.75%, with the average RMSE and MAE being 1.049 mm/day and 0.829 mm/day, respectively. 【Conclusion】 By using publicly accessible weather forecast data and an online-trained BP neural network model, real-time ET0 forecast can be achieved with high accuracy. This approach offers valuable support for farmers, enabling informed and timely irrigation decisions.

【Background and Objective】 The Yellow River Basin is the second largest river basin in China. Over the past 60 years, precipitation patterns in the basin have undergone both spatial and temporal changes due to the impact of climate change. This paper analyzes the precipitation characteristics and spatiotemporal alterations in dry-wet patterns in the basin. 【Method】 The study was based on meteorological data measured from 1960 to 2022 at weather stations across the basin. Spatiotemporal changes in precipitation, dry and wet spells were analyzed using regression analysis, correlation analysis, frequency percentage method, and the Mann-Kendall nonparametric statistical method. 【Result】 ① No significant change in total annual precipitation was identified in the basin from 1960 to 2022. However, a notable decrease in annual precipitation events (-1.95% per decade) and a significant increase in average precipitation intensity (+2.67% per decade) were observed. A sudden change in annual precipitation frequency occurred in 1978, while a shift in precipitation intensity happened in 1990. Since 1990, annual precipitation frequency has decreased markedly, while precipitation intensity has increased significantly. Regionally, the upper basin has become wetter, while the middle and lower reaches of the basin have become drier. Across the basin, light, moderate and medium-intensive rainfall events have declined, while heavy and extreme rainfall events have increased. Extreme rainfall event have become increasingly common, particularly in the middle and lower reaches of the basin. ② The risk of drought has increased in the upper and middle reaches of the basin, although drought intensity has decreased. Meanwhile, the likelihood and severity of drought in the lower reach has risen. Wet spells have shown heightened precipitation intensity during the wet season, corresponding to an increase in extreme precipitation events across the basin. The coexistence of drought and flooding highlights vulnerability of the basin to extreme weather events. ③ Changes in annual precipitation are primarily driven by intensive precipitation events, while changes in precipitation frequency ware influenced by low-intensive precipitation events. The distribution of wet days is affected by low-intensive precipitation, whereas wet-season precipitation is driven by high-intensive precipitation events. 【Conclusion】 In the context of climate change, the Yellow River Basin is increasingly vulnerable to both droughts and floods. The reduction in low-intensive precipitation events has prolonged dry periods, increasing drought risk. Simultaneously, the increase in precipitation intensity and extreme events during wet seasons raises flood risk. These findings underscore the urgent need for developing integrated water resource managements and new strategies to mitigate the impact of climate change.

【Objective】 Water flow in pumps is modeled using computational fluid dynamics (CFD). However, the impact of turbulence model on simulation accuracy remains unexplored. This study aims to address this gap. 【Method】 We simulated axial water flow in a pump using three turbulent models: the Standard k-ε, RNG k-ε and SST k-ω models. We compared the simulated results with experimental results and analyzed the internal flow fields simulated by different turbulence models. 【Result】 At steady flow state, the head curves simulated by all three turbulence models were consistent with the experimental curves. Under forward and reverse flow braking conditions, the head calculated by the Standard k-ε and SST k-ω model was close to or slightly higher than that calculated by the RNG k-ε model. Compared to the RNG k-ε model, the Standard k-ε and SST k-ω models reduced the maximum error of calculated head by 0.13 m and 0.22 m, and average error by 0.55 m and 0.48 m, respectively. Under low water flow condition in the forward pump, the maximum and average error of the calculated head by the standard k-ε model were the least; compared to the RNG k-ε and SST k-ω model, it reduced the maximum error of the calculated head by 1.19 m and 0.96 m, and the average error by 0.56 m and 0.68 m, respectively. Under reverse downstream braking conditions, the SST k-ω model was most accurate; compared to the Standard k-ε and RNG k-ε model, it reduced the maximum errors of the calculated head by1.08 m and 1.63 m, and average errors by 0.6 m and 0.9 m, respectively. Under the reverse pump condition, the Standard k-ε was most accurate; compared to the RNG k-ε and SST k-ω models, it reduced the maximum errors of the calculated head by 0.65 m and 0.74 m, and average errors by 0.34 m and 0.44 m, respectively. For other conditions, including positive rotating pump, positive rotating downstream braking, positive rotating and reverse rotating turbine, the simulated internal flow field in the pump was smooth, the simulated external characteristics were close to experimental observations, and simulated results of all three models were similar. 【Conclusion】 The accuracy of turbulence models for simulating axial water flow in pumps depends on operating conditions. Our results provide insights into selecting appropriate turbulence model for simulating water flow in pumps.

【Objective】 Shuozhou, located North of Shanxi province, has been significantly influenced by climate change. Since temperature is one of the most important factors influencing plant growth and evaporation of surface water and subsurface water, this paper analyses its spatiotemporal variation in this region. 【Method】 Using air temperature data measured from 1972 to 2023 at weather stations in six counties, districts, and cities in the region, we calculated both mean and extreme temperatures using statistical method, linear analysis, moving average method, Mann-Kendall test, and Morlet wavelet analysis. 【Result】 ① From 1972 to 2023, the average temperature, annual mean maximum temperature, and annual mean minimum temperature exhibited significant upward trends, increasing at rates of 0.4 ℃/decade, 0.39 ℃/decade and 0.53 ℃/decade, respectively. Nighttime temperature increases contributed most to this warming trend; daily average temperature had been decreasing. The warming trend was most pronounced in spring and winter; temperature increases were more significant from 1991 to 2020 than in other years. ② Extreme annual maximum and minimum temperatures did not show significant increases, with a change in 0.26 ℃/decade and 0.08 ℃/decade respectively. ③ Extreme maximum temperatures occurred mainly from late May to early August, peaking in late July. Extreme minimum temperatures typically appeared from late December to early February, with the highest frequency in January. ④ A marked temperature shift occurred in 1997, with a significant warming trend after 1998. Cold winters were more common before the 1990s, while warmer winters predominated after the 20th century. ⑤ Both mean and extreme temperatures in showed significant oscillations with periods of 2 to 4 years. 【Conclusion】 Our results could help improve irrigation and drainage management under the influence of climate change.

【Objective】 Agriculture is the largest water consumer in Shanxi Province. Understanding the water requirements of major crops is important for improving water resource management and the development of sustainable agriculture in the province. This paper studies the spatiotemporal variations in irrigation water requirements and drought assessment for major crops in the province. 【Method】 The reference evapotranspiration (ET0) was calculated using the Penman-Monteith formula based on daily meteorological data from 1960 to 2019 at 28 weather stations across the province. Using the single crop coefficient method, we calculated the water requirements (ETc) and irrigation requirements (IR) of spring maize, summer maize, spring wheat, winter wheat, cotton and potato. The spatial distributions of effective precipitation, temperature, ET0, ETc and IR for each crop were analyzed using ArcGIS to evaluate drought frequency and its impacts across the province. 【Result】 Spatially, ET0 and average daily temperature vary consistently across the province, decreasing from South to North up to Wutai and Youyu, then increasing further northward. Temporally, except for potato, the IR of other crops has been decreasing from 1960 to 2019, with their IR varying spatially. The average IR of major crops over the 1960—2019 period ranked as follows: winter wheat (293.23 mm) > spring wheat (144.74 mm) > spring maize (142.81 mm) > cotton (131.89 mm) > summer maize (82.81 mm) > potato (74.56 mm). During growing periods of crops, the influence of droughts, when occurring, were regional. 【Conclusion】 Precipitation can basically meet the demands of summer maize, spring maize and spring wheat need for irrigation during their rapid growth periods. However, winter wheat, cotton, and potato require irrigation during their mid-growth periods.

Rice weevil (Sitophilus oryzae L.) is one of the major pest of stored connodities. In this study the extracts from n-Hexane, dichloromethane (DCM) and methanol leaf and seed/fruit of Karanja, Pongamia pinnata (L.); Mahogany, Swietenia mahogani Jacq.; Neem, Azadirachta indica A. Juss and Urmoi, Sapium indicum Willd. at 2.0, 4.0, 6.0, 8.0 and 10.0% (w/v) concentrations were evaluated for their antifeedant effect against rice weevil, Sitophilus oryzae. The experiment was conducted with Completely Randomized Design with three replications at the Entomology Division, Bangladesh Institute of Nuclear Agriculture (BINA). All the extracts had moderate inhibitory effects on the feeding activities of rice weevil. Among the four plant extracts tested, urmoi possessed the highest feeding deterrent effect (126.52) whereas neem possessed the least deterrent effect (89.20). Mahogany and urmoi had the same and high level of efficacy (+++), whereas karanja and neem had same but low level of efficacy (++). The fruit extract had the highest total feeding deterrent effect (110.44) and the leaf extract had the least total feeding deterrent effect (98.68). The results showed that the fruit extracts were better than leaf extracts. Among the three solvents, the highest total coefficient of deterrency was observed in methanol extract (116.83). The coefficient of deterrency increased proportionally with the increase of doses. The results of this study shows that 10% urmoi plant extracts able to protect rice weevil in storage condition for their strong antifeedant effect.

In flowering plants, callose (β-1,3-glucan) plays a vital role in pollen development, especially in the separation and development of microspores. However, the molecular mechanism of callose deposition during rice pollen development remains unclear. In this study, we isolated and characterized a novel rice pollen defective mutant, non-separated microspores 1 (nsm1), which produced “dyad” or “tetrad” pollen grains. Cytological analysis indicated disrupted interstitial callose deposition at the cell plate of dyads and tetrads in nsm1 pollens. This disruption caused sporopollenin to be massively deposited outside of the junction where the interstitial callose wall connected with the peripheral callose wall, or unevenly distributed on the interstitial pollen primexine at the late meiosis stage. Consequently, an excess tectum-like layer was formed outside of the junction, connecting with the tectum of two microspores during later developmental stages, which prevented the separation of microspores. Additionally, in the linkage area, the tectum of two microspores gradually fused or degenerated, resulting in a decreased contact area between microspores and the anther locule. Therefore, the defect in callose deposition resulted in unsuccessful separation of microspores, abnormal deposition of pollen exine, and also affected the accumulation of materials in microspores, resulting in pollen semi-sterility. NSM1, encoding a callose synthase located in the Golgi body, is ubiquitously expressed in anthers with its peak expression at the young microspore stage. The in vitro enzyme activity assay confirmed that NSM1 possesses callose synthase activity, and the enzyme activity in the nsm1 mutants was significantly reduced. Phylogenetic analysis indicated that NSM1 and its orthologs play a highly conserved role in callose biosynthesis among plant species. Taken together, we propose that NSM1 plays an essential role in male meiotic callose synthesis and later pollen wall development.

Fusarium head blight (FHB) threatens wheat production worldwide. Utilization of FHB resistant varieties is the most effective solution for disease control. Owing to the limited sources of FHB resistance, mining of novel resistance genes is crucial. Here, we report an FHB resistance gene from a wild wheat relative species, Roegneria ciliaris and developed FHB resistant germplasm containing this gene. Wheat-R. ciliaris disomic addition line DA3Sc showed enhanced type II FHB resistance compared to its sister line 3Sc-Null without chromosome 3Sc, indicating that the resistance was contributed by the addition of 3Sc. The resistance gene on 3Sc was validated using F2 and F2:3 populations derived from the cross between DA3Sc and susceptible Aikang 58 (a susceptible cultivar), demonstrating that the lines with 3Sc had significantly enhanced FHB resistance compared to the individuals without 3Sc. This was the second resistance gene identified in R. ciliaris, designated FhbRc2. To transfer FhbRc2 to common wheat, we produced a double-monosomic chromosome population by crossing DA3Sc with the Chinese Spring nulli-tetrasomic line N3DT3B. Eight alien chromosome lines containing 3Sc were identified using genomic/fluorescence in situ hybridization and 3Sc-specific marker analysis. Only the lines carrying the long arm of 3Sc conferred FHB resistance, further locating FhbRc2 on 3ScL. A compensating wheat-R. ciliaris Robertsonian translocation line T3DS·3ScL harboring FhbRc2 is developed and provides a potential genetic resource in wheat breeding for enhanced FHB resistance.