Crop Conditions Research Articles

Exploring the Impact of Pesticide Usage on Crop Condition: A Causal Analysis of Agricultural Factors

Human lifestyle is affected by the agricultural development in the last 12,000 years ago. The development of agriculture is one of the reasons that global population surged. To ensure sufficient food production for supporting human life, pesticides as a more effective and economical tools, are extensively used to enhance the yield quality and boost crop production. This study investigated the factors that affect crop production and whether the factors of pesticide usage are the most important factors in crop production using the dataset from Kaggle that provides information based on crops harvested by various farmers. Logistic regression is used to investigate the relationship between various factors and crop production. However, the logistic regression is unable to deal with predictors that are related to each other and identifying the greatest impact factor. Therefore, causal discovery is applied to address the above limitations. The result of causal discovery showed that crop condition is greatly impacted by the estimated insects count, where estimated insects count is affected by the factors of pesticide usage. This study enhances our understanding of the influence of pesticide usage on crop production and contributes to the progress of agricultural practices.

Evaluating the potential of multi-temporal Sentinel-1 and Sentinel-2 data for regional mapping of olive trees

ABSTRACT Olives are a crucial economic crop in Mediterranean countries. Detailed spatial information on the distribution and condition of crops at regional and national scales is essential to ensure the continuity of crop quality and yield efficiency. However, most earlier studies on olive tree mapping focused mainly on small parcels using single-sensor, very high resolution (VHR) data, which is time-consuming, expensive and cannot feasibly be scaled up to a larger area. Therefore, we evaluated the performance of Sentinel-1 and Sentinel-2 data fusion for the regional mapping of olive trees for the first time, using the Izmir Province of Türkiye, an ancient olive-growing region, as a case study. Three different monthly composite images reflecting the different phenological stages of olive trees were selected to separate olive trees from other land cover types. Seven land-cover classes, including olives, were mapped separately using a random forest classifier for each year between 2017 and 2021. The results were assessed using the k-fold cross-validation method, and the final olive tree map of Izmir was produced by combining the olive tree distribution over two consecutive years. District-level areas covered by olive trees were calculated and validated using official statistics from the Turkish Statistical Institute (TUIK). The K-fold cross-validation accuracy varied from 94% to 95% between 2017 and 2021, and the final olive map achieved 98% overall accuracy with 93% producer accuracy for the olive class. The district-level olive area was strongly related to the TUIK statistics (R2 = 0.60, NRMSE = 0.64). This study used Sentinel data and Google Earth Engine (GEE) to produce a regional-scale olive distribution map that can be scaled up to the entire country and replicated elsewhere. This map can, therefore, be used as a foundation for other scientific studies on olive trees, particularly for the development of effective management practices.

Эффективность систем основной обработки почвы в экспериментальном севообороте в аридных условиях Нижнего Поволжья

The article presents the results of research on the formation of crop yields depending on the depth of tillage in the arid conditions of the Lower Volga region. Basic tillage is an important agrotechnical technique that affects many indicators, including crop yields. The aim of the research was to study the influence of basic tillage (classical and energy-saving) on crop productivity, as well as on the process of creating optimal water-physical properties of the soil of short-rotation crop rotations in conditions of insufficient moisture. The research was carried out at the experimental field of Rossorgo in 2021-2023. The experiment traces the dependence of the main agrophysical properties of the soil, the contamination of crops and crop yields on the applied techniques. It is proved that plowing is the most effective in the rain-fed conditions of the Lower Volga region. With the minimization of the main tillage, yields decrease, with the use of disking – by 10...30%, on variants with cultivation – by 14...51%, as well as deterioration of the water-physical properties of the soil and the phytosanitary condition of crops. Keywords: TILLAGE, PRODUCTIVITY, PLOWING, DISKING, CULTIVATION

3D-ResNet-BiLSTM Model: A Deep Learning Model for County-Level Soybean Yield Prediction with Time-Series Sentinel-1, Sentinel-2 Imagery, and Daymet Data

Ensuring food security in precision agriculture requires early prediction of soybean yield at various scales within the United States (U.S.), ranging from international to local levels. Accurate yield estimation is essential in preventing famine by providing insights into food availability during the growth season. Numerous deep learning (DL) algorithms have been developed to estimate soybean yield effectively using time-series remote sensing (RS) data to achieve this goal. However, the training data with short time spans can limit their ability to adapt to the dynamic and nuanced temporal changes in crop conditions. To address this challenge, we designed a 3D-ResNet-BiLSTM model to efficiently predict soybean yield at the county level across the U.S., even when using training data with shorter periods. We leveraged detailed Sentinel-2 imagery and Sentinel-1 SAR images to extract spectral bands, key vegetation indices (VIs), and VV and VH polarizations. Additionally, Daymet data was incorporated via Google Earth Engine (GEE) to enhance the model’s input features. To process these inputs effectively, a dedicated 3D-ResNet architecture was designed to extract high-level features. These enriched features were then fed into a BiLSTM layer, enabling accurate prediction of soybean yield. To evaluate the efficacy of our model, its performance was compared with that of well-known models, including the Linear Regression (LR), Random Forest (RF), and 1D/2D/3D-ResNet models, as well as a 2D-CNN-LSTM model. The data from a short period (2019 to 2020) were used to train all models, while their accuracy was assessed using data from the year 2021. The experimental results showed that the proposed 3D-Resnet-BiLSTM model had a superior performance compared to the other models, achieving remarkable metrics (R2 = 0.791, RMSE = 5.56 Bu Ac−1, MAE = 4.35 Bu Ac−1, MAPE = 9%, and RRMSE = 10.49%). Furthermore, the 3D-ResNet-BiLSTM model showed a 7% higher R2 than the ResNet and RF models and an enhancement of 27% and 17% against the LR and 2D-CNN-LSTM models, respectively. The results highlighted our model’s potential for accurate soybean yield predictions, supporting sustainable agriculture and food security.

Geohash-Based Maize Plant Monitoring System Ulitizing Drones

Corn is one of the important food crops in the world. To ensure optimal results, farmers usually monitor crop conditions manually. Unfortunately, manual monitoring can take time and effort due to the large area of maize fields (approx.: 1 ha). In addition, corn plants are also susceptible to diseases and pests which often result in corn farmers experiencing losses due to crop failure. This can be supported by several cases of corn crop failure in Lampung caused by pests and water shortages, such as in Bumidaya Village, South Lampung. Therefore, this research will develop a corn crop monitoring system using geohash and drones. The primary objective of this research is to develop a comprehensive design for a corn crop monitoring system, leveraging the capabilities of machine learning for corn plant recognition. The application of geohash is expected to assist farmers in handling and early detection of plants that experience a decrease in health quality before it spreads to all other maize crops. The results of the model training carried out with the R-CNN are that the detection model is able to detect with an accuracy of 88.9% with a low distance of the drone in taking pictures or close to plants.

Evaluating the Effects of Controlled Drainage on Nitrogen Uptake, Utilization, Leaching, and Loss in Farmland Soil

Controlling drainage during the growth stage is one of the means to provide suitable water and fertilizer conditions for crops, alleviate environmental pollution, and increase crop yield. Therefore, in this study, we studied three drainage treatments: free drainage (FD) and growth-stage subsurface controlled drainage (CD) at depths of 40 cm (CWT1) and 70 cm (CWT2). We used the HYDRUS-2D model to simulate the dynamic changes of NO3-N in the 0–100 cm soil layer as well as NO3-N uptake by crops, leaching after irrigation and fertilization, and loss through subsurface pipes in 2020 (model calibration period) and 2021 (model validation period). The degree of agreement between the simulated and measured values was high, indicating a high simulation accuracy. CD increased the soil NO3-N content and crop NO3-N uptake, and decreased NO3-N leaching and loss. We observed significant differences in the soil NO3-N content after irrigation at the budding stage of oilseed sunflower between CD and FD, with the largest difference seen for the 0–40 cm soil layer. CD increased crop yield, and the average oilseed sunflower yield of the CWT1 and CWT2 treatments increased by 4.52% and 3.04% relative to the FD treatment (p < 0.05). CD also enhanced nitrogen use efficiency. In moderately salinized soil, CD at 40 cm (CWT1) reduced the nutrient difference in vertical and horizontal directions while retaining water and fertilizer. CWT1 stabilized the groundwater depth, reduced the hydraulic gradient of groundwater runoff, and decreased the drainage flow rate. The NO3-N leaching and loss dropped, which promoted crop nitrogen uptake and utilization, improved nitrogen use efficiency, reduced nitrogen loss, and had a positive effect on protecting the soil and water environment. The results demonstrate that CD is a suitable drainage method for the experimental area.

Windbreak effectiveness of shelterbelts with different characteristic parameters and arrangements by means of CFD simulation

Shelterbelts can locally reduce wind speed in open terrains, moderating wind induced damage for agriculture and ameliorating growing conditions for crops. In this paper, Computation Fluid Dynamics (CFD) is adopted to clarify the effects of the characteristic parameters and arrangement of shelterbelts on their windbreak efficiency. The Darcy-Forchheimer canopy model is adopted to represent the vegetation, so modeling it as an isotropic porous medium. The shelterbelts are immersed in wind representative of the one expected in the atmospheric boundary layer and the numerical model is firstly validated against full-scale data. The parameters governing the shelterbelt aerodynamic performance, including shelterbelt width, shelterbelt length and opening length, are systematically varied, so providing a clear overview of the effects induced by their modification. The results show that the sheltering effect is enhanced as the shelterbelt width increases, while the effects of shelterbelt length are less pronounced in comparison. When there is opening inside the shelterbelt, the mean wind speed and turbulence kinetic energy behind the canopy is significantly increased due to the passage of airflow. General considerations regarding efficient shelterbelt arrangements are deduced by a comparison among L-, U- and rectangular-shaped types.

A Markov chain model of crop conditions and intrayear crop yield forecasting

AbstractCrop condition reports are an important source of information for producers, grain traders, businesses, and policymakers to assess and manage the price and yield risk inherent in a given crop. A Markov chain model is proposed for describing the weekly dynamic behavior of reported crop conditions. Empirical transition probabilities are estimated for corn grown in Nebraska, and forecasted crop conditions from the Markov chain are used as inputs to forecast final crop yields prior to harvest time. The results suggest that the modeling and forecasting approach has value for estimating crop yields as intrayear information about crop conditions materializes.

Гербологічний стан посівів сільськогосподарських культур у короткоротаційній сівозміні залежно від систем основного обробітку ґрунту та удобренн

Гербологічний стан посівів сільськогосподарських культур у короткоротаційній сівозміні залежно від систем основного обробітку ґрунту та удобренн

Mathematical model to assess the impacts of aflatoxin contamination in crops, livestock and humans

Aflatoxin contamination poses a significant challenge in food safety and security as it affects both the health of consumers and supply chains. Due to the health impacts associated with aflatoxin contamination, countries have set standards and restrictions for importing food crops and animal feed, resulting in greater economic losses to farmers, transporters, and crop processors. This study aimed to develop a mathematical model that tracks the contamination status of crops, livestock and humans in supporting efforts to control aflatoxin. The analysis of the mathematical model shows that both aflatoxin contamination-free equilibrium (ACFE) and aflatoxin contamination-persistence equilibrium (ACPE) exist. To study the dynamics of contamination, we derived the basic aflatoxin contamination number, R0 which is analogous to the basic reproduction number in epidemiological models. When R0<1, the ACFE is globally asymptotically stable, whereas when R0>1 the ACPE is globally asymptotically stable. Partial Rank Correlation Coefficients (PRCCs) for global sensitivity analysis were calculated using Latin Hypercube Sampling (LHS) to see how sensitive and significant the parameter is on each variable. Results from numerical simulations showed that decreasing crop contamination and shading rates and increasing the death rate of aflatoxin fungi in soil by 50% can reduce the basic contamination number by above 92%. Thus, it is important to introduce control measures that target crop contamination, shading and death rates of aflatoxin fungi in soil to reduce contamination in the population. Compared to other studies in aflatoxin contamination, the current study provides a thoroughly global sensitivity analysis of parameters involved in contamination and indicated the most important ones for control strategies.

Functional and flavour properties of de-oiled flours and dry-enriched protein concentrates of lupin and soy

Dry fractionation is low in energy and water use and thus a sustainable option to obtain protein-rich ingredients. Air-classification is used to remove starch from legumes, and electrostatic separation can be used to remove fibres from other starting materials like oilseeds. Flour from oil-rich crops needs to be de-oiled to facilitate dry fractionation, which involves the use of organic solvents or mechanical pressing and might affect the ingredient functionality. This research evaluated if the functionality of soy and lupin is affected by solvent de-oiling and electrostatic separation. Industrially toasted soy and lupin flour contained native protein, which was preserved upon electrostatic separation, de-oiling with hexane and de-oiling with acetone, but ethanol de-oiling resulted in protein denaturation. De-oiling with ethanol or hexane is preferred over de-oiling with acetone based on the flavour profile after de-oiling. The use of different solvents affected the solubility to a different extent and electrostatic separation resulted in a similar or higher solubility of the ingredients (22% - 49%), dependent on the solvent, crop and pH condition used. Overall, electrostatic separation resulted in protein-enriched ingredients (43%DM - 67%DM Nfactor=5.7) that show potential for application as stabilizers in emulsion-based systems like dressings or frozen foam-based systems like ice-cream.

Incremental learning for crop growth parameters estimation and nitrogen diagnosis from hyperspectral data

Nitrogen is an essential nutrient in crop growth cycle and directly affects the photosynthesis of crops. The leaf chlorophyll content (LCC) and leaf area index (LAI) are important for characterizing photosynthetic capacity of crops and are critical indicators for diagnosing nitrogen status of crops. Hyperspectral remote sensing technology provided a means to achieve crop LCC and LAI estimation. However, the redundancy of spectral data and canopy structure effect can cause poor robustness of the estimating models, further hindering the development and application of estimating models across different crop species. In this study, a method based on incremental learning was proposed for the simultaneous estimation of LCC and LAI, and for nitrogen diagnosis across crops. First, for the spectral dataset generated by the PROSAIL model, a deep neural network was used to construct LCC and LAI estimation models (called DNNCA model). Secondly, for the hyperspectral data collected from field crops (soybean, canola and wheat), incremental learning using regularization (LwF algorithm) was used to update the DNNCA model parameters. Finally, the dilution curve model based on the LCC-LAI anisotropic growth relationship was developed to assess crop nitrogen status. The results showed that: (1) The constrained bi-objective optimizated DNNCA model can consider the interactive effect of LAI and LCC on spectral reflectance, and achieved reliable estimation on PROSAIL simulation data set (LAI:R2 = 0.82, RMSE = 0.77 m2/m2; LCC:R2 = 0.91, RMSE = 6.4 ug/cm2). (2) By incremental learning, DNNCA model has continuous learning capability and stable estimation on cross-crop (canola, soybean and wheat) field-measured data (LAI:R2 = 0.64–0.82, RMSE = 0.58–1.02 m2/m2; LCC:R2 = 0.56–0.82, RMSE = 3.9–10.5 ug/cm2). (3) The relationship between the NNILCC and the NNILNC was significant. The NNILCC derived from the anisotropy relationship between crop LAI and LCC was an effective tool for crop nitrogen status diagnosis. (4) The process of crop LAI, LCC and NNILCC reflected the effect of water and nitrogen supply on crop growth. The appropriate water-nitrogen treatments contributed to LAI increase and LCC accumulation. The study demonstrated that hyperspectral remote sensing technology combined with incremental learning is an effective method for cross-crop growth monitoring and nitrogen diagnosis. These results provide a reference and basis for filed water-nitrogen supply and management.

Robots addressing agricultural labour shortages and environmental issues

PurposeThe purpose of this paper is to provide details of recent developments in agricultural robots with an emphasis of those that address labour shortages and environmental issues.Design/methodology/approachFollowing an introduction which highlights some of the challenges facing the agricultural industry, this discusses recent robotic agricultural vehicle developments and the enabling technologies. It then provides examples of terrestrial and airborne robots employed in precision agricultural practices. Finally, brief conclusions are drawn.FindingsTraditional, labour-intensive and environmentally harmful agricultural practices are not sustainable in the long term, and if food supply is to meet future demand, radical changes will be required. Exploiting recent advances in artificial intelligence (AI), agricultural equipment manufacturers are developing robotic vehicles in response to labour shortages. Precision agricultural practices will mitigate many of the detrimental environmental impacts and can also reduce the reliance on manpower. Weeding robots which reduce or eliminate the use of herbicides have been commercialised by a growing number of companies and again exploit AI techniques. Drones equipped with imaging device are playing an increasingly important role by characterising agricultural and crop conditions, thereby allowing highly targeted agrochemical application.Originality/valueThis illustrates how the agricultural industry is adopting robotic technology in response to the need to increased productivity while mitigating the problems of shortages of labour and environmental degradation.

Study Progress on the Ecological Effects of Biochar in Soil

Soil is an indispensable and important part of human production and life, and biochar, as an important form of utilization of biomass energy, can be widely used in soil conditioning and crop conditioning. Currently, a systematic framework for studying biochar's ecological impact on soil is still required. This paper briefly introduces the basic preparation method of biochar, structural characterization of biochar, and briefly describes its basic properties. By adsorbing pollutants with a high specific surface area and strong adsorption, biochar can be used to clean up heavy metal contamination in soil. It can also boost soil fertility by increasing enzyme activity and organic carbon content, as well as by reducing bulk weight and increasing cation exchange to increase soil fertility retention capacity. In addition the moderate addition of biochar can also play a positive role in crop conditioning to a certain extent. The purpose of this paper is to summarize the basic theories and research related to the study of the ecological effects of biochar in soil by introducing the typical preparation methods and basic properties of biochar, so as to facilitate the subsequent further research.

A new multispectral index for canopy nitrogen concentration applicable across growth stages in ryegrass and barley

Accurately monitoring Canopy Nitrogen Concentration (CNC) is a prerequisite for precision nitrogen (N) fertiliser management at the farm scale with carbon and N budgeting across the landscape and ecosystems. While many spectral indices have been proposed for CNC monitoring, their applicability and accuracy are often adversely affected by confounding factors such as aboveground biomass (AGB), crop type, growth stages, and environmental conditions, limiting their broader application and adoption; with AGB being one of the most dominant signals and confounding factors at canopy scale. The confounding effect can become more challenging as AGB is also physiologically linked with CNC across the growth stages. Additionally, the interplay between index form, selection of optimal wavebands and their bandwidths remains poorly understood for CNC index design. This study proposes robust and cost-effective 2- and 4-waveband multispectral (MS) CNC indices applicable across a wide range of crop conditions. We collected 449 canopy reflectance spectra (400–980 nm) together with corresponding CNC and AGB measurements across four growth stages of ryegrass (winter and summer), and five growth stages of barley (winter-spring) in Victoria, Australia, in 2018 and 2019. All possible waveband (400–980 nm) combinations revealed that the best combination varied between seasons and crop types. However, the visible spectrum, particularly the blue region, presented high and consistent performance. Bandwidths of 10–40 nm outperformed either very narrow (2 nm) or very broad bandwidths (80 nm). The newly developed 2-waveband index (416 and 442 nm with 10-nm bandwidth; R2 = 0.75 and NRMSE = 0.2) and 4-waveband index (512, 440, 414 and 588 nm with 40-nm bandwidth; R2 = 0.81 and NRMSE = 0.17) exhibited the best performance, while validation with an independent dataset (from a different growing period to those used in the model development) obtained NRMSE values of 0.25 and 0.24, respectively. The 4-waveband index provides enhanced performance and permits use of broader bandwidths than its 2-waveband counterpart.

Multispectral Vegetation Indices and Machine Learning Approaches for Durum Wheat (Triticum durum Desf.) Yield Prediction across Different Varieties

Durum wheat (Triticum durum Desf.) is one of the most widely cultivated cereal species in the Mediterranean basin, supporting pasta, bread and other typical food productions. Considering its importance for the nutrition of a large population and production of high economic value, its supply is of strategic significance. Therefore, an early and accurate crop yield estimation may be fundamental to planning the purchase, storage, and sale of this commodity on a large scale. Multispectral (MS) remote sensing (RS) of crops using unpiloted aerial vehicles (UAVs) is a powerful tool to assess crop status and productivity with a high spatial–temporal resolution and accuracy level. The object of this study was to monitor the behaviour of thirty different durum wheat varieties commonly cultivated in Italy, taking into account their spectral response to different vegetation indices (VIs) and assessing the reliability of this information to estimate their yields by Pearson’s correlation and different machine learning (ML) approaches. VIs allowed us to separate the tested wheat varieties into different groups, especially when surveyed in April. Pearson’s correlations between VIs and grain yield were good (R2 &gt; 0.7) for a third of the varieties tested; the VIs that best correlated with grain yield were CVI, GNDVI, MTVI, MTVI2, NDRE, and SR RE. Implementing ML approaches with VIs data highlighted higher performance than Pearson’s correlations, with the best results observed by random forest (RF) and support vector machine (SVM) models.

Three-dimensional reconstruction of cotton plant with internal canopy occluded structure recovery

The inner leaves of crop canopies are obscured by outer branches and leaves, leading to information loss and observation difficulty of the occluded canopy structure using modern crop monitoring techniques. It has restricted the development of phenotypic analysis and precision agriculture. In this paper, we propose a neural network approach to reconstruct the occluded structure of crop canopies with an RGB-D sensor. Taking the cotton plant as the object of study, we propose a novel Cascade Leaf Segmentation and Completion Network (CLSCN) to reconstruct the occluded leaf images and propose a Fragmental Leaf Point–cloud Reconstruction Algorithm (FLPRA) to complete the missing point clouds. By combining the Instance Segmentation Network (ISN), Generative Adversarial Network (GAN) and Point-cloud Reconstruction Algorithm (PRA), the three-dimensional models of cotton plants with both completed internal and external structures of the canopy are smoothly reconstructed. Firstly, we collect a large number of leaf images and point clouds of cotton plants using an RGB-D sensor with the top view and construct a manually labeled cotton leaf dataset for training and evaluation. Secondly, a network named CLSCN is cascading constructed with an Instance Segmentation Network (ISN) and a Generative Adversarial Network (GAN), and the two parts of CLSCN are separately trained with our constructed dataset to output complete cotton leaves. Thirdly, with the fusion of the completed RGB images output by cascaded network segmentation and the point clouds captured by RGB-D sensor, the proposed FLPRA is used to filter, reconstruct, fuse and register the cotton canopy leaf point clouds, and to obtain the whole cotton canopy point-clouds with inner occluded structure recovery. Finally, the CLSCN and FLPRA are validated using the validation dataset of cotton leaf. The test results indicate that the front-end ISN of the proposed CLSCN can generate high-quality cotton leaf masks, with FID scores less than 35 and mIoU up to 84.65%. Additionally, the back-end GAN of CLSCN can complete the occluded leaves with an accuracy of over 94%. The reconstruction accuracy of the final three-dimensional model of the cotton canopy is as high as 82.70%. Therefore, the proposed neural network and algorithm effectively solve the problem of incomplete canopy point cloud caused by the occlusion of outer leaves and provide an effective way to recover the complete three-dimensional structure of crop canopy with internal occlusion. It is a meaningful theoretical and technical support to realize real-time crop status observation and precise field management in agriculture production.

Determination of the number of clusters of normalized vegetation indices using the k-means algorithm

The process of clustering of normalized vegetation indices in five regions with a total area of 2565 hectares of the North Kazakhstan region was studied. A methodological approach to organizing the clustering process is proposed using the vegetation indices NDVI, MSAVI, ReCI, NDWI and NDRE, taking into account individual characteristics in the three main phases of spring wheat development As a result of the research, vegetation indices were grouped into 3 classes using the k-means clustering method. The first cluster contained vegetation indices whose maximum values occupied about 33.98% of the total area of the study area. It was found that NDVImax located in the first cluster was positively correlated with soil-corrected vegetation indices MSAVI and crop moisture indicators NDMI (R2=0.92). The second cluster is characterized by minimum values of NDVImax coefficients at the germination, tillering and ripening phases (from 0.53 to 0.55). The lowest values of vegetation indices occupied 35.9 % in the germination phase, 37.9 % in the tillering phase, and 40.1 % of the field from the total area. The third cluster is characterized by average values of vegetation indices in all three phases. A correlation matrix was also constructed to assess the closeness of the relationship between actual yield and NDVI vegetation indices. The maximum coefficient was obtained at the germination phase, R=0.94 with a minimum significance coefficient p=0.018. The approach used in this study can be useful in the analysis of satellite data, as it can improve the sensitivity of the constellation procedure. From a practical point of view, the results obtained make it possible to assess the condition of agricultural crops in the early stages of the growing season, which makes it possible to improve their productivity based on the results of cluster analysis

Foliar Application of Melatonin Positively Affects the Physio-Biochemical Characteristics of Cotton (Gossypium hirsutum L.) under the Combined Effects of Low Temperature and Salinity Stress.

Low temperature and soil salinization during cotton sowing and seedling adversely affect cotton productivity. Exogenous melatonin (MT) can alleviate the damage caused to plants under non-biological stress; thus, applying MT is a means to improve the growth condition of crops under stress. However, achieving this goal requires a thorough understanding of the physiological regulatory mechanisms of MT on cotton seedlings under low temperature and salinity stress. This study could bring new knowledge on physio-biochemical mechanisms that improve the tolerance of cotton seedlings to combined effects of low temperature and salt stress using an exogenous foliar application of MT. The phytotron experiment comprised two temperature levels of cold stress and control and five MT treatments of 0, 50, 100, 150, and 200 μM and two salinity levels of 0 and 150 mM NaCl. Compared with the control treatments (non-salinity stress under cold stress and control), the combined stress of salt and low temperature reduced cotton seedlings' biomass and net photosynthetic rate (Pn), aggravated the membrane damage, reduced the potassium (K+) content, and increased the sodium (Na+) accumulation in the leaves and roots. Under NaCl stress, exogenously sprayed 50-150 μM MT increased the biomass and gas exchange parameters of cotton seedlings under salt and low temperature combined with salt stress, reduced the degree of membrane damage, and regulated the antioxidant enzyme, ion homeostasis, transport, and absorption of cotton seedlings. The pairwise correlation analysis of each parameter using MT shows that the parameters with higher correlation with MT at cold stress are mainly malondialdehyde (MDA), peroxidase (POD), and catalase (CAT). The highest correlation coefficient at 25 °C is observed between the K+ and Na+ content in cotton seedlings. The conclusion indicates that under salt and low-temperature stress conditions, exogenous application of MT primarily regulates the levels of Pn, superoxide dismutase (SOD), andPOD in cotton seedlings, reduces Na+ and MDA content, alleviates damage to cotton seedlings. Moreover, the most significant effect was observed when an exogenous application of 50-150 μM of MT was administered under these conditions. The current study's findings could serve as a scientific foundation for salinity and low-temperature stress alleviation during the seedling stage of cotton growth.

Cropping System Stability Drives SOC Sequestration and Increases Saturation Deficit in Hot Arid Durum Wheat Cropping Systems

Stability analysis has been extensively employed in evaluating the genotype x environment interaction, but it can also be conveniently applied to cropping systems. This study applied stability analysis to a long-term experiment (1998–2016) conducted in an experimental farm in southern Italy. The experiment involved two standard semi-arid cropping systems (WW—durum wheat monocropping and WP—durum wheat–field pea rotation), with two nitrogen fertilization levels (60 kg ha−1—N60 and 120 kg ha−1—N120), along with an unfertilized control (N0). Soil organic carbon (SOC) was estimated in all treatments at the end of the experiment to investigate the potential for SOC sequestration and soil C saturation by analyzing the stability of all investigated cropping conditions in relation to SOC sequestration. The study also investigated the relationship between C input and SOC sequestration. SOC sequestration per year was higher in WW than in WP, confirming that including legumes in the rotation did not enhance the SOC content. Similarly, the positive impact of nitrogen application on biomass yield did not affect SOC accumulation. The stability analysis revealed that over the long term, WW exhibited greater stability than WP, suggesting that, in semi-arid environments, a more stable cropping system can enhance soil carbon sequestration, with nitrogen fertilization exerting a scarce influence. Segmented regression analysis identified two distinct patterns of SOC accumulation based on C input, separated by a definite threshold of C input (breakpoint) quantified at 14.7 Mg ha−1. It was found that the steady-state C level, achieved after the accumulation of this C input, was reached much faster in WW than in WP, in which it could only be inferred at 23 years. In conclusion, this study underscores the role of cropping system stability in driving SOC sequestration. Furthermore, it emphasizes the potential of stability analysis in assisting stakeholders in refining their strategies for different cropping conditions in dry and rainy environments.