Crop Growth Research Articles

Short-term effects of co-applied biochar and paper mill biosolids on soil microbial communities under field conditions

Biochar and paper mill biosolids (PB) are reported to improve soil fertility, crop growth and indirectly conditions for soil microbial communities. However, it is unclear how the co-application of these materials impacts soil microbial communities under field conditions. A study was initiated in 2018 in Québec, QC, Canada and fine roots and rhizosphere soil were sampled to determine the effects of co-application of wood biochar (0, 10, and 20 Mg dry wt. ha−1) and PB (0 and 30 Mg wet wt. ha−1) on the percentage of corn (Zea mays L.) and soybean (Glycine max L. Merr.) root colonization by arbuscular mycorrhizal fungi (AMF), soil microbial biomass phosphorus (MBP) and microbial (Bacteria, Fungi and AMF) diversity and community structure in a temperate loamy soil. Co-applying PB and biochar increased soil MBP compared with the control and biochar-only application. Applying PB alone or with biochar increased the level of root colonization by AMF compared with the control and biochar-only application in soybean but not in corn. Overall, biochar and PB application had no significant effect on bacterial and AMF diversity and community structure compared with the control. However, applying PB alone or with biochar decreased the fungal alpha diversity (Shannon and Simpson indices), affected several fungal taxa abundances and shifted the fungal community structure as indicated by the principal coordination analysis (PCoA). Our results provided an understanding on the short-term effects of co-applied wood biochar and PB on microbial communities of a temperate loamy soil under field conditions, as well as scientific bases for further investigation.

Research advances on the metabolic pathways and functions mediated by plant xanthine dehydrogenase

The xanthine dehydrogenase (XDH), a molybdenum-containing oxidoreductase belonging to the molybdenum hydroxylase flavoprotein family, has been identified in a variety of eukaryotes, bacteria, and archaea. XDH catalyzes the conversion of xanthine and hypoxanthine to uric acid, which then undergoes further reactions to form allantoin and allantoic acid. Studies have shown that XDH plays a role in various metabolic processes, including purine metabolism, nitrogen metabolism, hormone metabolism, reactive oxygen species metabolism, and responses to biotic and abiotic stresses. Here, we introduced the structural features, metabolic pathways, and biological functions of XDH. In addition, we summarized the research progress in XDH to give insights into the molecular mechanism of purine metabolism in plants and prospected the application of XDH, with the aim to facilitate future research on the growth, development, and stress resistance of crops.

An Extended Method Based on the Geometric Position of Salient Image Features: Solving the Dataset Imbalance Problem in Greenhouse Tomato Growing Scenarios

Machine vision has significant advantages in a wide range of agricultural applications; however, acquiring a large number of high-quality image resources is often challenging in actual agricultural production due to environmental and equipment conditions. Therefore, crop image augmentation techniques are particularly important in crop growth analysis. In this paper, greenhouse tomato plants were used as research subjects to collect images of their different fertility stages with flowers and fruits. Due to the different durations of each fertility period, there is a significant difference in the number of images collected. For this reason, this paper proposes a method for balanced amplification of significant feature information in images based on geometric position. Through the geometric position information of the target in the image, different segmentation strategies are used to process the image and supervised and unsupervised methods are applied to perform balanced augmentation of the image, which is combined with the YOLOv7 algorithm to verify the augmentation effect. In terms of the image dataset, the mixed image dataset (Mix) is supplemented with mobile phone images on top of in situ monitoring images, with precision increased from 70.33% to 82.81% and recall increased from 69.15% to 81.25%. In terms of image augmentation, after supervised balanced amplification, the detection accuracy is improved from 70.33% to 77.29%, which is suitable for supervised balanced amplification. For the mobile phone dataset (MP), after amplification, it was found that better results could be achieved without any amplification method. The detection accuracy of the mixed dataset with different data sources matching the appropriate amplification method increased slightly from 82.81% to 83.59%, and accurate detection could be achieved when the target was shaded by the plant, and in different environments and light conditions.

THE IMPACT OF SEED SIZE ON INITIAL DROUGHT STRESS RESILIENCE AND YIELD IN WHEAT CULTIVATION

Wheat yield is affected severely by drought in this era of changing climate patterns, including high temperatures and altered precipitation patterns. Drought is among the most challenging environmental stressors, limiting wheat cultivars' growth, productivity, and performance. The current study was conducted during the rabi season 2022 at the Research Area, Department of Agronomy, University of the Punjab, Lahore, Pakistan. Therefore, the present study evaluated the potential of diverse seed sizes to advance wheat crop growth, development, and yield when subjected to different drought levels. The study comprised two experiments. The first was a lab experiment that included different drought levels (DL), DL0: 0.0 bar, DL1: -2 bar, DL2: -4 bar, and DL3: -6 bar (drought levels were induced by solutions of PEG-6000 at different concentrations) and three wheat seed size classes, i.e., bold grain (>38 g), medium grain (<33 g), and small grain (<25 g). In the field experiment, drought stress levels were DL0 (regular irrigation), DL1 (first irrigation at 30 days), DL2 (first irrigation at 45 days), and DL3 (first irrigation at 60 days). Seed sizes included W1 (bold >38 g), W2 (medium <33 g), and W3 (small <25 g). Drought severity increased with DL1 to DL3. The outcomes of the field experiment revealed that varying levels of drought stress and seed size classes significantly affected parameters such as emergence time, growth traits, biomass allocation, tiller count, plant height, and grain and biomass outcomes. Bold seeds contributed to higher biomass and grain yield, while severe drought decreased yields. Notably, the Harvest Index was affected, indicating bold seeds allocate more biomass to grains. In conclusion, proper seed size selection, favouring bold seeds, can enhance resilience to drought, benefiting wheat cultivation in water-scarce regions.

Boron confers salt tolerance through facilitating BnaA2.HKT1-mediated root xylem Na+ unloading in rapeseed (Brassica napus L.).

Boron (B) is an important limiting factor for plant growth and yield in saline soils, but the underlying molecular mechanisms remain poorly understood. In this study, we found that appropriate B supply obviously complemented rapeseed (Brassica napus L.) growth under salinity accompanied by higher biomass production and less reactive oxygen species accumulation. Determination of Na+ content in shoots and roots indicated that B significantly repressed root-to-shoot Na+ translocation, and non-invasive micro-tests of root xylem sap demonstrated that B increased xylem Na+ unloading in the roots of rapeseed plants under salinity. Comparative transcriptomic profiling revealed that B strongly upregulated BnaHKT1s expression, especially BnaA2.HKT1, in rapeseed roots exposed to salinity. In situ hybridizations analysis showed that BnaA2.HKT1 was significantly induced in root stelar tissues by high B (HB) under salinity. Green fluorescent protein and yeast heterologous expression showed that BnaA2.HKT1 functioned as a plasma membrane-localized Na+ transporter. Knockout of BnaA2.HKT1 by CRISPR/Cas9 resulted in hypersensitive of rapeseed plants to salinity even under HB condition, with higher shoot Na+ accumulation and lower biomass production. By contrast, overexpression of BnaA2.HKT1 ameliorated salinity-induced growth inhibition under B deficiency and salinity. Overall, our results proposed that B functioned as a positive regulator for the rapeseed growth and seed production under salt stress through facilitating BnaA2.HKT1-mediated root xylem Na+ unloading. This study may also provide an alternative strategy for the improvement of crop growth and development in saline soils.

Radiation Limits the Yield Potential of Main Crops Under Selected Agrivoltaic Designs—A Case Study of a New Shading Simulation Method

Agrivoltaics (APVs) represent a growing technology in Europe that enables the co-location of energy and food production in the same field. Photosynthesis requires photosynthetic active radiation, which is reduced by the shadows cast on crops by APV panels. The design of the module rows, material, and field orientation significantly influences the radiation distribution on the ground. In this context, we introduce an innovative approach for the effective simulation of the shading effects of various APV designs. We performed an extensive sensitivity analysis of the photovoltaic (PV) geometry influence on the ground-incident radiation and crop growth of selected cultivars. Simulations (2013–2021) for three representative arable crops in eastern Austria (winter wheat, spring barley, and maize) and seven different APV designs that only limited to the shading effect showed that maize and spring barley experienced the greatest annual above-ground biomass and grain yield reduction (up to 25%), with significant differences between the APV design and the weather conditions. While spring barley had similar decreases within the years, maize was characterized by high variability. Winter wheat had only up to a 10% reduction due to shading and a reduced photosynthetic performance. Cold/humid/cloudy weather during the growing season had more negative yield effects under APVs than dry/hot periods, particularly for summer crops such as maize. The lowest grain yield decline was achieved for all three crops in the APV design in which the modules were oriented to the east at a height of 5 m and mounted on trackers with an inclination of +/−50°. This scenario also resulted in the highest land equivalent ratios (LERs), with values above 1.06. The correct use of a tracker on APV fields is crucial for optimizing agricultural yields and electricity production.

Advances in Nanotechnology for Sustainable Agriculture: A Review of Climate Change Mitigation

Currently, one of the main challenges is the mitigation of the effects of climate change on the agricultural sector. Conventional agriculture, with the intensive use of herbicides and pesticides to control weeds and pests, and the improper use of mineral fertilizers, contributes to climate change by causing increased greenhouse gases and groundwater pollution. Therefore, more innovative technologies must be used to overcome these problems. One possible solution is nanotechnology, which has the potential to revolutionize the conventional agricultural system. Active nanoparticles can be used both as a direct source of micronutrients and as a delivery platform for bioactive agrochemicals to improve crop growth, yield, and quality. The use of nanoparticle formulations, including nano-pesticides, nano-herbicides, nano-fertilizers, and nano-emulsions, has been extensively studied to improve crop health and shelf-life of agricultural products. Comprehensive knowledge of the interactions between plants and nanoparticles opens up new opportunities to improve cropping practices through the enhancement of properties such as disease resistance, crop yield, and nutrient use. The main objective of this review is to analyze the main effects of climate change on conventional agricultural practices, such as the use of pesticides, herbicides, and fertilizers. It also focuses on how the introduction of nanoparticles into conventional practices can improve the efficiency of chemical pest control and crop nutrition. Finally, this review examines in depth the last 10 years (2014–2024) of scientific literature regarding the use of nanoparticles in agriculture to mitigate the effects of climate change.

A comprehensive review of integrating biostimulants and biopesticides for organic berry farming: exploring challenges and opportunities for Africa

Agriculture plays a pivotal role in Africa, contributing significantly to sustainable farming practices and the establishment of resilient food systems. Within this context, the use of various types of biostimulants, including microbial biostimulants such as Plant Growth-Promoting microorganisms (PGPM) and non-microbial products like Algal extract, humic acid, and protein hydrolysates, as well as biopesticides, emerges as a promising strategy to bolster sustainable agriculture, particularly in the realm of organic berry production. These substances have the potential to enhance crop growth, fortify stress tolerance, and optimize nutrient absorption, benefiting both human health and the environment. This paper aims to explore the opportunities and challenges associated with incorporating plant biostimulants into organic berry production within the African agricultural sector. To achieve this objective, an extensive and comprehensive review encompassing scientific literature, policy documents, and global data was conducted. The primary focus of this review was to investigate the current state of biostimulant adoption in organic berry farming within the African agricultural sector, with a specific emphasis on identifying potential opportunities and discussing the benefits derived from their application. Additionally, we addressed the challenges encountered and proposed practical approaches to achieving sustainable agriculture. The findings and conclusions of our review reveal the transformative potential of biostimulants in organic berry production. The evidence points to remarkable advancements in plant growth, plant health, overall yield, and fruit nutritional quality. By implementing these substances, we can also minimize the ecological footprint of agricultural practices. However, several challenges remain, including limited accessibility, insufficient awareness and knowledge regarding biostimulant usage, and a shortage of research specific to African agriculture. To overcome these challenges and achieve sustainable agriculture, this paper recommends practical approaches such as raising awareness, investing in research and development, and promoting the use of biostimulants through policy interventions and capacity-building programs. We underscore the importance of stakeholder participation and local adaptations for effectively integrating biostimulants in African agriculture. The significance of integrating plant biostimulants in organic berry production lies in advancing sustainable agriculture. This paper aims to explore the opportunities and challenges associated with incorporating plant biostimulants into organic berry production within Africa.

Physiological and transcriptomic analysis reveals the coating of microcapsules embedded with bacteria can enhance wheat salt tolerance

Salt stress is one of the most important abiotic stress factors limiting crop production. Therefore, improving the stress resistance of seeds is very important for crop growth. Our previous studies have shown that using microcapsules encapsulating bacteria (Pontibacter actiniarum DSM 19842) as seed coating for wheat can alleviate salt stress. In this study, the genes and pathways involved in the response of wheat to salt stress were researched further. The results showed that compared with the control, the coating can improve osmotic stress and decrease oxidative damage by increasing the content of proline (29.1%), the activity of superoxide dismutase (SOD) (94.2%), peroxidase (POD) (45.7%) and catalase (CAT) (3.3%), reducing the content of hydrogen peroxide (H2O2) (39.8%) and malondialdehyde (MDA) (45.9%). In addition, ribonucleic acid (RNA) sequencing data showed that 7628 differentially expressed genes (DEGs) were identified, and 4426 DEGs up-regulated, 3202 down-regulated in the coated treatment. Many DEGs related to antioxidant enzymes were up-regulated, indicating that coating can promote the expression of antioxidant enzyme-related genes and alleviate oxidative damage under salt stress. The differential gene expression analysis demonstrated up-regulation of 27 genes and down-regulation of 20 genes. Transcription factor families, mostly belonging to bHLH, MYB, B3, NAC, and WRKY. Overall, this seed coating can promote the development of sustainable agriculture in saline soil.

Estimating Winter Canola Aboveground Biomass from Hyperspectral Images Using Narrowband Spectra-Texture Features and Machine Learning

Aboveground biomass (AGB) is a critical indicator for monitoring the crop growth status and predicting yields. UAV remote sensing technology offers an efficient and non-destructive method for collecting crop information in small-scale agricultural fields. High-resolution hyperspectral images provide abundant spectral-textural information, but whether they can enhance the accuracy of crop biomass estimations remains subject to further investigation. This study evaluates the predictability of winter canola AGB by integrating the narrowband spectra and texture features from UAV hyperspectral images. Specifically, narrowband spectra and vegetation indices were extracted from the hyperspectral images. The Gray Level Co-occurrence Matrix (GLCM) method was employed to compute texture indices. Correlation analysis and autocorrelation analysis were utilized to determine the final spectral feature scheme, texture feature scheme, and spectral-texture feature scheme. Subsequently, machine learning algorithms were applied to develop estimation models for winter canola biomass. The results indicate: (1) For spectra features, narrow-bands at 450~510 nm, 680~738 nm, 910~940 nm wavelength, as well as vegetation indices containing red-edge narrow-bands, showed outstanding performance with correlation coefficients ranging from 0.49 to 0.65; For texture features, narrow-band texture parameters CON, DIS, ENT, ASM, and vegetation index texture parameter COR demonstrated significant performance, with correlation coefficients between 0.65 and 0.72; (2) The Adaboost model using the spectra-texture feature scheme exhibited the best performance in estimating winter canola biomass (R2 = 0.91; RMSE = 1710.79 kg/ha; NRMSE = 19.88%); (3) The combined use of narrowband spectra and texture feature significantly improved the estimation accuracy of winter canola biomass. Compared to the spectra feature scheme, the model’s R2 increased by 11.2%, RMSE decreased by 29%, and NRMSE reduced by 17%. These findings provide a reference for studies on UAV hyperspectral remote sensing monitoring of crop growth status.

Actinobacteria derived from soybean/corn intercropping influence the subsequent wheat

Soil legacy effects, especially soil bacterial legacy effects, influence growth, fitness and nutrient acquisition in sequential cropping systems. To date, mechanisms underlying soil bacterial legacy influences on subsequent crops remain largely unknown. In this study, we employed soybean monoculture (S), corn monoculture (C), and soybean/corn intercropping (SC) to study soil legacy effects on the growth and nutrient acquisition of wheat. In these tests, S, C and SC drove establishment of distinctive soil bacterial communities, with higher abundances of Actinobacteria and Proteobacteria taxa observed in SC plots than in S and C treatments. Variation among soil bacterial communities was associated with functional shifts in nitrogen cycling in SC treatment compared to other treatments（C and Control）. Soil legacy effects in turn may contribute to growth, nutrient acquisition and grain production in wheat crops planted in rotations. Pot assay suggest that soil microorganism of SC treatment significantly increased the plant height of wheat by 15.1 % and 18.7 %, the shoot biomass by 50.7 % and 62.7 %, the nitrogen content by 76.0 % and 94.9 %, the phosphorus content by 80.3 % and 75.9 %, and the potassium content by 64.0 % and 83.7 % by compared with C and S. Actinobacteria taxa collections further promote nutrient acquisition of wheat. Taken together, our observations from field plots and manipulation of specific bacterial taxa revealed novel soil bacterial legacy effects of previously reared crops on subsequent crops. These new insights open avenues for using soil legacy effects for positive impacts in crop rotation systems.

A Fuzzy Logic Approach to Performance Evaluation of Genomics-Driven Crop Improvement

Genomics-driven crop improvement has emerged as a revolutionary approach to enhance yield, resilience, and nutritional value in agriculture. However, traditional performance evaluation methods often must catch up with agricultural systems' complex, multifaceted nature. This study proposes a fuzzy logic-based framework to assess the performance of genomics-driven crop improvement initiatives. By integrating fuzzy logic, we can effectively manage uncertainty and ambiguity inherent in agricultural data, allowing for a more nuanced evaluation of crop traits, environmental factors, and management practices. The paper begins by elucidating the fundamental principles of genomics-based agriculture and its impact on crop development. It then delves into the theoretical underpinnings of fuzzy logic and its applicability in modeling the multifaceted aspects of crop growth, encompassing genetic, environmental, and physiological factors. Through a synthesis of existing research and empirical data, the paper illustrates the effectiveness of fuzzy logic in capturing the nuances of crop development processes, including germination, flowering, and yield formation. This paper underscores the pivotal role of fuzzy logic analysis in advancing genomics-based agriculture, offering insights into the dynamic interactions shaping crop development and facilitating informed decision-making for sustainable and resilient food production systems in the face of evolving environmental challenges. Results indicate that the fuzzy logic approach enhances the accuracy of performance assessments and facilitates better decision-making in crop management.

Effects of nano-MoO3 on growth, quality and toxicity of soybean.

Metal nanoparticles are widely used in agricultural production. As a new type of molybdenum fertilizer, MoO3NPs have the properties of nanomaterials and the characteristics of molybdenum nutrition. Previous studies have focused on their role in promoting crop growth. However, it is unknown whether excessive MoO3NPs will affect crop quality and nutritional value. In this study, the effects of different concentrations of MoO3NPs (0, 0.15, 0.5, 1.0, 5.0, 10, 50, 100 mg kg-1) on the growth and quality of soybean were investigated by pot experiments to analyze the plant effects caused by MoO3NPs. The results showed that the effects of MoO3NPs treatment on plant biomass and nodule number were promoted at low concentrations (0.15-5 mg kg-1) and inhibited at high concentrations (10-100 mg kg-1). According to the logistic distribution model, it was predicted that MoO3NPs would have the strongest toxic effect on soybean flowering stage. The contents of MoO3NPs which reduced the yield of soybean by 10% and 20% were 12.38 and 30.81 mg kg-1. NP0.15 could significantly improve the total amount of amino acids in grains, while NP100 reduced the total amount of amino acids in grains, both of them significantly increasing the contents of linolenic acid and linoleic acid in soybean seeds. A change of MoO3NPs concentration had no negative effect on the nutritional value of soybean grains. The research could lead to a better understanding of the potential impact of nutritional changes caused by MoO3NPs on human health. © 2024 Society of Chemical Industry.

Atmospheric footnotes: Ada Lovelace on climate

This article brings to light correspondence between Augusta Ada King, Countess of Lovelace (1815–1852) and Sir John Frederick William Herschel (1792–1871) from the Royal Society archives. The 1848 letters reveal the extent of her contribution to an article authored by her husband, William King, Lord Lovelace (1805–1893), on the role of climate on crop growth. This involvement uncovers little-known facets of her scientific pursuits: her interest in the application of photography to meteorological instrumentation, her budding study of astronomy, and her development of mathematical treatments to model the environmental conditions required for plants to grow. The exchange situates Ada Lovelace in the larger context of Victorian science and reflects more widely on the complicated access of women to institutional science.

Experimental and numerical evaluation of soil water and salt dynamics in a corn field with shallow saline groundwater and crop-season drip and autumn post-harvest irrigations

In areas with shallow saline groundwater, soil salts inevitably accumulate in the root zone during the growth period due to irrigation and upward movement of salts from the groundwater. In Northern China, autumn irrigation (AIR) with large amounts of water is commonly employed post-harvest to mitigate soil salt stress on crop growth in the subsequent year. Optimizing the total irrigation depth during both crop-growth and non-growth periods is challenging because of the movement of soil salts, which is influenced by their two-dimensional distribution around drippers and the impact of the winter freeze-thaw cycles, significantly affecting water flow and solute transport during winter. In this study, the HYDRUS-1D and HYDRUS-2D models were integrated and calibrated using experimental data collected from 2021 to 2023 in China's Ordos south bank irrigation area. This model integration was conducted to assess soil water and salt dynamics during the non-growth and corn-growth periods under different irrigation strategies: a) AIR with high (AH) and low (AL) irrigation depths, and b) drip irrigation (DIR) with high (DH), medium (DM), and low (DL) irrigation depths. The results indicated that HYDRUS effectively modeled the electrical conductivity of the saturation paste extract (ECe) across different irrigation strategies, yielding an average coefficient of determination (R2) and the root mean square errors (RMSE) of 0.87 and 0.53 dS m−1, respectively. Generally, ECe increased during the growth period with DIR and decreased during the non-growth period with AIR. For the 0–40 cm soil layer, ECe decreased by 5.7 % and 12 % for every 100 mm increase in the AIR and DIR depths, respectively. Compared with the AHDM and AHDL treatments, reducing an AIR depth and increasing a DIR depth resulted in lower ECe in the 0–40 cm layer during the growth period and higher crop yield (CY) and irrigation water productivity (WPI). Specifically, the average ECe in the 0–40 cm layer decreased by 4.8 % during the growth period in the ALDH treatment compared to the AHDM treatment, and CY and WPI increased by 7.2 % and 10.3 %, respectively. Additionally, the irrigation strategy was the most effective in reducing ECe when AIR accounted for 35 % of the total irrigation. This study suggested that combining low AIR and high DIR could enhance water and field productivity.

Simulation of water-salt transport and balance in cultivated-wasteland system based on SWAP model in Hetao Irrigation District of China

Water and salt transport among different use type land is important to irrigation management in arid area. In this study, a typical irrigation unit including cultivated land and wasteland in Hetao Irrigation District of China was selected to explore water and salt transport between cultivated land and wasteland system. Soil water-salt dynamics, groundwater depth and salinity were observed within the period of crop growing and autumn irrigation period in 2018 and 2019. Calibrated SWAP model was used to simulate water and salt flux of cultivated land and wasteland during the crop growing period and autumn irrigation period. Furthermore, water-salt transport exchange capacity was calculated between cultivated land and wasteland system in study area. Groundwater was recharged by soil water and soil salt was leached in cultivated land during the crop growing period. Soil water was recharged by groundwater and soil salt was accumulated in saline wasteland during the crop growing period. During the crop growing period, the total leaching of soil salinity was 15.64 t·ha−1 in cultivated land. Soil salt accumulation was 2.03 t·ha−1 in saline wasteland. During the autumn irrigation period, the total leaching of soil salinity was 14.93 t·ha−1 in cultivated land. Total leaching of soil salinity was 1.56 t·ha−1 in saline wasteland. Overall, saline wasteland had an important role to receive salt from cultivated land and maintain salt dynamic balance in arid irrigation area with shallow groundwater.

Using Nanomaterials and Arbuscular mycorrhizas to Alleviate Saline–Alkali Stress in Cyperus esculentus (L.)

Saline–alkali (SA) stress is an abiotic stress that exists widely in the natural environment, seriously affecting the growth and development of crops. Tiger nut (Cyperus esculentus L.), a perennial herb of Cyperus in Cyperaceae, is considered a pioneer crop for growing and improving SA land due to its excellent adaptability and SA tolerance. This study is the first to evaluate the SA tolerance of tiger nut and the alleviative effects of nanomaterials (nano-selenium and multi-walled carbon nanotubes) and arbuscular mycorrhizas (AMs) on SA stress. The results showed that the seedling fresh weight of tiger nut was the most suitable parameter to describe the dose–response effect of plant growth with increased SA concentration. Based on the log-logistic dose–response curve, the GR50 values of NaCl and NaHCO3 (the concentrations causing a 50% reduction in seedling fresh weight) were determined to be 163 mmol L−1 and 63 mmol L−1, respectively. Under these stresses, the exogenous application of MWCNTs at 100 mg L−1 or Nano-Se at 10 mg L−1 showed that the effect of SA on tiger nut was alleviated. Field evaluation further showed that the exogenous application of MWCNTs, Nano-Se, or AMs could effectively alleviate SA stress on tiger nut. Compared to the untreated control, the application of these substances significantly improved the plant photosynthesis-related parameter, antioxidant enzyme activity, plant height (height: 66.0–69.9 cm), tuber yield (yield: 23.4–27.4 g plant−1), and oil quality of tiger nut under SA stress. The results of this study indicate that the application of MWCNTs, Nano-Se, or AMs, to tiger nut can alleviate SA stress and maintain seed yield, providing the possibility of using these nanoparticles to improve the SA tolerance of tiger nut in agricultural practice.

A Novel Interpolation Method for Soil Parameters Combining RBF Neural Network and IDW in the Pearl River Delta

Soil fertility is a critical factor in agricultural production, directly impacting crop growth, yield, and quality. To achieve precise agricultural management, accurate spatial interpolation of soil parameters is essential. This study developed a new interpolation prediction framework that combines Radial Basis Function (RBF) neural networks with Inverse Distance Weighting (IDW), termed the IDW-RBFNN. This framework initially uses the IDW method to apply preliminary weights based on distance to the data points, which are then used as input for the RBF neural network to form a training dataset. Subsequently, the RBF neural network further trains on these data to refine the interpolation results, achieving more precise spatial data interpolation. We compared the interpolation prediction accuracy of the IDW-RBFNN framework with ordinary Kriging (OK) and RBF methods under three different parameter settings. Ultimately, the IDW-RBFNN demonstrated lower error rates in terms of RMSE and MRE compared to direct RBF interpolation methods when adjusting settings based on different power values, even with a fixed number of data samples. As the sample size decreases, the interpolation accuracy of OK and RBF methods is significantly affected, while the error of IDW-RBFNN remains relatively low. Considering both interpolation accuracy and resource limitations, we recommend using the IDW-RBFNN method (p = 2) with at least 60 samples as the minimum sampling density to ensure high interpolation accuracy under resource constraints. Our method overcomes limitations of existing approaches that use fixed steady-state distance decay parameters, providing an effective tool for soil fertility monitoring in delta regions.

The Effect of a Parcel-Aggregated Cropping Structure Mapping Method in Irrigation-Water Estimation in Arid Regions—A Case Study of the Weigan River Basin in Xinjiang Province

Effective management of agricultural water resources in arid regions relies on precise estimation of irrigation-water demand. Most previous studies have adopted pixel-level mapping methods to estimate irrigation-water demand, often leading to inaccuracies when applied in arid areas where land salinization is severe and where poorly growing crops cause the growing area to be smaller than the sown area. To address this issue and improve the accuracy of irrigation-water demand estimation, this study utilizes parcel-aggregated cropping structure mapping. We conducted a case study in the Weigan River Basin, Xinjiang Province, China. Deep learning techniques, the Richer Convolutional Features model, and the bilayer Long Short-Term Memory model were applied to extract parcel-aggregated cropping structures. By analyzing the cropping patterns, we estimated the irrigation-water demand and calculated the supply using statistical data and the water balance approach. The results indicated that in 2020, the cultivated area in the Weigan River Basin was 5.29 × 105 hectares, distributed over 853,404 parcels with an average size of 6202 m2. Based on the parcel-aggregated cropping structure, the estimated irrigation-water demand ranges from 25.1 × 108 m3 to 30.0 × 108 m3, representing a 5.57% increase compared to the pixel-level estimates. This increase highlights the effectiveness of the parcel-aggregated cropping structure in capturing the actual irrigation-water requirements, particularly in areas with severe soil salinization and patchy crop growth. The supply was calculated at 24.4 × 108 m3 according to the water balance approach, resulting in a minimal water deficit of 0.64 × 108 m3, underscoring the challenges in managing agricultural water resources in arid regions. Overall, the use of parcel-aggregated cropping structure mapping addresses the issue of irrigation-water demand underestimation associated with pixel-level mapping in arid regions. This study provides a methodological framework for efficient agricultural water resource management and sustainable development in arid regions.

Design and Use of a Stratum-Based Yield Predictions to Address Challenges Associated with Spatial Heterogeneity and Sample Clustering in Agricultural Fields Using Remote Sensing Data

Machine learning (ML) models trained with remote sensing data have the potential to improve cereal yield estimation across various geographic scales. However, the complexity and heterogeneity of agricultural landscapes present significant challenges to the robustness of ML-based field-level yield estimation over large areas. In our study, we propose decomposing the landscape complexity into homogeneous zones using existing landform, agroecological, and climate classification datasets, and subsequently applying stratum-based ML to estimate cereal yield. This approach was tested in a heterogeneous region in northern Morocco, where wheat is the dominant crop. We compared the results of the stratum-based ML with those applied to the entire study area. Sentinel-1 and Sentinel-2 satellite imagery were used as input variables to train three ML models: Random Forest, Extreme Gradient Boosting (XGBoost), and Multiple Linear Regression. The results showed that the XGBoost model outperformed the other assessed models. Furthermore, the stratum-based ML approach significantly improved the yield estimation accuracy, particularly when using landform classifications as homogeneous strata. For example, the accuracy of XGBoost model improved from R2 = 0.58 and RMSE = 840 kg ha−1 when the ML models were trained on data from the entire study area to R2 = 0.72 and RMSE = 809 kg ha−1 when trained in the plain area. These findings highlight that developing stratum-based ML models using landform classification as strata leads to more accurate predictions by allowing the models to better capture local environmental conditions and agricultural practices that affect crop growth.