Search Results (1,825)

Passion fruit is a valued tropical fruit crop that faces environment-related growth strains. TCP genes are important for both growth modulation and stress prevention in plants. Herein, we systematically analyzed the TCP gene family in passion fruit, recognizing 30 members. Genes exhibiting closer phylogenetic relationships exhibited similar protein and gene structures. Gene members of the TCP family showed developmental-stage- or tissue-specific expression profiles during the passion fruit life cycle. Transcriptome data also demonstrated that many PeTCPs showed induced expression in response to hormonal treatments and cold, heat, and salt stress. Based on transcriptomics data, eight candidate genes were chosen for preferential gene expression confirmation under cold stress conditions. The qRT-PCR assays suggested PeTCP15/16/17/19/23 upregulation, while PeTCP1/11/25 downregulation after cold stress. Additionally, TCP19/20/29/30 exhibited in silico binding with cold-stress-related miRNA319s. GFP subcellular localization assays exhibited PeTCP19/1 were localized at the nucleus. This study will aid in the establishment of novel germplasm, as well as the further investigation of the roles of PeTCPs and their cold stress resistance characteristics. Full article

A range of remote sensing platforms provide high spatial and temporal resolution insights which are useful for monitoring vegetation growth. Very few studies have focused on fruit orchards, largely due to the inherent complexity of their structure. Fruit trees are mixed with inter-rows that can be grassed or non-grassed, and there are no standard protocols for ground measurements suitable for the range of crops. The assessment of biophysical variables (BVs) for fruit orchards from optical satellites remains a significant challenge. The objectives of this study are as follows: (1) to address the challenges of extracting and better interpreting biophysical variables from optical data by proposing new ground measurements protocols tailored to various orchards with differing inter-row management practices, (2) to quantify the impact of the inter-row at the Sentinel pixel scale, and (3) to evaluate the potential of Sentinel 2 data on BVs for orchard development monitoring and the detection of key phenological stages, such as the flowering and fruit set stages. Several orchards in two pedo-climatic zones in southeast France were monitored for three years: four apricot and nectarine orchards under different management systems and nine cherry orchards with differing tree densities and inter-row surfaces. We provide the first comparison of three established ground-based methods of assessing BVs in orchards: (1) hemispherical photographs, (2) a ceptometer, and (3) the Viticanopy smartphone app. The major phenological stages, from budburst to fruit growth, were also determined by in situ annotations on the same fields monitored using Viticanopy. In parallel, Sentinel 2 images from the two study sites were processed using a Biophysical Variable Neural Network (BVNET) model to extract the main BVs, including the leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of green vegetation cover (FCOVER). The temporal dynamics of the normalised FAPAR were analysed, enabling the detection of the fruit set stage. A new aggregative model was applied to data from hemispherical photographs taken under trees and within inter-rows, enabling us to quantify the impact of the inter-row at the Sentinel 2 pixel scale. The resulting value compared to BVs computed from Sentinel 2 gave statistically significant correlations (0.57 for FCOVER and 0.45 for FAPAR, with respective RMSE values of 0.12 and 0.11). Viticanopy appears promising for assessing the PAI (plant area index) and FCOVER for orchards with grassed inter-rows, showing significant correlations with the Sentinel 2 LAI (R² of 0.72, RMSE 0.41) and FCOVER (R² 0.66 and RMSE 0.08). Overall, our results suggest that Sentinel 2 imagery can support orchard monitoring via indicators of development and inter-row management, offering data that are useful to quantify production and enhance resource management. Full article

Maize, a globally significant cereal, is increasingly cultivated under challenging environmental conditions, necessitating innovations in sustainable agriculture. This study evaluates the synergistic effects of a novel technique combining a Bacillus velezensis A6 strain with a plant extract from the Lamiales order on maize growth and stress resilience. Employing a pilot field trial, this study was conducted on the “La Añoreta” experimental farm of the ECONATUR group, where various biostimulant treatments, including bacterial and plant extract applications, were tested against a control group. The treatments were applied during key vegetative growth stages (V10-Tenth-Leaf, VT-Tassel, R1-Silking) and monitored for effects on plant height, biomass, and fumonisin content. The results suggest that the combined treatment of Bacillus velezensis A6 and the plant extract increases maize height (32.87%) and yield (62.93%) and also reduces fumonisin concentrations, improving its resistance to stress, compared to the control and other treatments. This study highlights the potential of microbial and botanical biostimulants and its novel combination for improving crop productivity and sustainability, suggesting that such synergistic combinations could play a crucial role in enhancing agricultural resilience to environmental stresses. Full article

Ecuadorian cocoa possesses important organoleptic characteristics such as aroma and flavor, called fine and aromatic cocoa. The objective of this study was to evaluate the initial growth responses of young cocoa seedlings to glyphosate in a dose progression in 45 cocoa plants (5 months old), which were transplanted into pots with substrate adjusted to pH 6.0–6.5. Glyphosate doses (0 to 904 g e.e. ha⁻¹) were applied every two weeks, evaluating the impact at 30 and 60 days post-application. Data on shikimate accumulation parameters, chlorophyll content and PSII quantum efficiency were subjected to multivariate analysis using a three-dimensional scatter plot. The results indicated that high concentrations of glyphosate contributed to higher shikimate concentration and lower PSII quantum efficiency. The findings for the variables crop damage, stem height and stem diameter were evaluated by ANOVA. Similarities were reported between the results of the variables height and diameter, and significant differences (p < 0.05) in the variable crop damage for all treatments were also reported. In terms of phytotoxic reaction and growth parameters, the most efficient treatment was DO4, since the seedlings with this dosage showed a low percentage of damage (10%) and the best indices in terms of height and diameter. The least efficient treatment was D15. The control plants (DO1) showed a crop damage of >50% because the absence of control favored weed proliferation. These indications highlight the need to adjust glyphosate doses according to the specific needs of each crop and the development stage of the plant in order to reduce negative effects and maximize potential benefits. Full article

Foxtail millet is a traditional minor crop in China, known for its strong resistance to stress, tolerance to barren lands, and wide adaptation. Phosphorus is an essential element for crop growth and development, and the appropriate application of phosphorus can enhance crop yield and quality. However, the optimal phosphorus fertilization levels for the growth of foxtail millet have yet to be determined. This study aims to explore the effects of different phosphorus application levels (T₁, T₂, T₃, and T₄), on phosphorus accumulation and use efficiency and crop yield and quality in the foxtail millet cultivars ‘B376’ and ‘B27’, which have different phosphorus efficiencies. Additionally, we investigated the effects of phosphorus accumulation and use efficiency on the heading and filling stages of these cultivars. The results show that the total phosphorus content and accumulation levels in the ‘B376’ and ‘B27’ cultivars vary at different developmental stages and in different plant parts. Furthermore, crop yield and quality in both cultivars vary in response to the different phosphorus application levels. In terms of yield, the phosphorus-tolerant variety ‘B376’ reaches its highest at T₂, while the phosphorus-sensitive variety ‘B27’ achieves its maximum yield at T₃. For quality, ‘B376’ exhibits the highest moisture and crude fat content under T₄, and the highest protein and the lowest amylose content under T₃. On the other hand, ‘B27’ achieves its highest moisture content under T₄, its highest crude fat and protein levels under T₃, and its lowest amylose content under T₂. Therefore, the response to different phosphorus application levels differs between the two cultivars with different phosphorus use efficiencies. Moreover, under different phosphorus fertilization levels, the average crop yield, moisture, fat, and amylose content averages of the phosphorus-tolerant ‘B376’ cultivar are 16.1%, 1.2%, 7.0%, and 4.1% higher than those of the phosphorus-sensitive ‘B27’ cultivar. Additionally, phosphorus use efficiency is positively correlated with the moisture and crude fat contents of foxtail millet. In conclusion, the phosphorus-tolerant cultivar demonstrates superior phosphorus accumulation, absorption, and utilization capacities compared to the phosphorus-sensitive cultivar. These results suggest that in the phosphorus-tolerant ‘B376’, optimal phosphorus fertilization levels enhance the development of roots, stems, and leaves at the T₂ (P₉₀) level, and promote the accumulation of moisture and crude fat in foxtail millet grains, thereby improving their taste and quality. Our findings provide a theoretical basis for phosphorus fertilizer utilization in foxtail millet cultivation and will help determine the optimal fertilization levels for foxtail millet growth. Full article

Zinc deficiency is one of the most widespread nutritional problems, affecting nearly one-third of the world population. In addition, it is known that zinc deficiency not only reduces crop yield but also its quality. The effect of different methods of zinc application on the growth, yield, and quality of safflower seeds under regular irrigation and interruption of irrigation from flowering to harvest (82 and 80 DAS in the first and second years, respectively) was evaluated. Zinc sulfate was applied in both soil and foliar methods. The zinc sulfate treatments include no zinc sulfate, soil application of 20, 40, and 60 kg ha⁻¹ at the planting stage; spraying 2.5, 5, and 7.5 g L⁻¹ in the rosette stage; and spraying 2.5, 5, and 7.5 g L⁻¹ in the flowering stage. The end-season drought caused a decrease in the chlorophyll index, leaf area index, relative water content, plant height, yield components, biological yield, seed yield, harvest index, seed oil content, oil harvest index, and seed element content compared to regular irrigation. The decrease in yield occurred with a decrease in the capitol number and diameter, seed number per capitol, and 1000-seed weight. The severity of the damage of the end-season drought stress in the second year was higher than in the first year due to the higher temperatures and the decrease in the rainfall. In both years, the application of zinc sulfate in different ways had an increasing effect on the studied traits in both normal and stress conditions. The application of zinc sulfate reduced the negative effects of unfavorable environmental conditions and improved the yield and nitrogen, phosphorus, potassium, zinc, and iron element content in the seed. In both application methods of zinc sulfate, the increment in the zinc sulfate concentration decreased the seed phosphorus content. However, the phosphorous content was more than that of the treatment of non-zinc application. The application of zinc increased the biological, seed, and oil yield of the treated plants, but the seed and oil yield were more affected. This effect was shown in the seed and oil harvest index increment. Under regular irrigation, higher concentrations of zinc sulfate enhanced plant performance, but under stress conditions, medium and lower concentrations were more effective. The highest 1000-seed weight and potassium and zinc content were obtained by spraying zinc sulfate at 5 g L⁻¹ in the flowering stage under normal irrigation conditions. A comparison of the two methods of applying zinc sulfate showed that foliar spraying was more effective than soil application in improving the seed yield. The soil application is more effective on biological yield than seed yield. Full article

Flooding is a frequent environmental stress that reduces soybean growth and grain yield in many producing areas in the world, such as the United States, Southeast Asia, and Southern Brazil. In these regions, soybean is frequently cultivated in lowland areas in crop rotation with rice, which provides numerous technical, economic, and environmental benefits. In this context, the identification of the most important spectral variables for the selection of more flooding-tolerant soybean genotypes is a primary demand within plant phenomics, with faster and more reliable results enabled using multispectral sensors mounted on unmanned aerial vehicles (UAVs). Accordingly, this research aimed to identify the optimal UAV-based multispectral vegetation indices for characterizing the response of soybean genotypes subjected to flooding and to test the best linear model fit in predicting tolerance scores, relative maturity group, biomass, and grain yield based on phenomics analysis. Forty-eight soybean cultivars were sown in two environments (flooded and non-flooded). Ground evaluations and UAV-image acquisition were conducted at 13, 38, and 69 days after flooding and at grain harvest, corresponding to the phenological stages V8, R1, R3, and R8, respectively. Data were subjected to variance component analysis and genetic parameters were estimated, with stepwise regression applied for each agronomic variable of interest. Our results showed that vegetation indices behave differently in their suitability for more tolerant genotype selection. Using this approach, phenomics analysis efficiently identified indices with high heritability, accuracy, and genetic variation (>80%), as observed for MSAVI, NDVI, OSAVI, SAVI, VEG, MGRVI, EVI2, NDRE, GRVI, BNDVI, and RGB index. Additionally, variables predicted based on estimated genetic data via phenomics had determination coefficients above 0.90, enabling the reduction in the number of important variables within the linear model. Full article

Flooding stress caused by climate change is a serious threat to crop productivity. To enhance our understanding of flooding stress in soybean, we analyzed the transcriptome of the roots of soybean plants after waterlogging treatment for 10 days at the V2 growth stage. Through RNA sequencing analysis, 870 upregulated and 1129 downregulated differentially expressed genes (DEGs) were identified and characterized using Gene Ontology (GO) and MapMan software (version 3.6.0RC1). In the functional classification analysis, “alcohol biosynthetic process” was the most significantly enriched GO term in downregulated DEGs, and phytohormone-related genes such as ABA, cytokinin, and gibberellin were upregulated. Among the transcription factors (TFs) in DEGs, AP2/ERFs were the most abundant. Furthermore, our DEGs encompassed eight soybean orthologs from Arabidopsis and rice, such as 1-aminocyclopropane-1-carboxylate oxidase. Along with a co-functional network consisting of the TF and orthologs, the expression changes of those genes were tested in a waterlogging-resistant cultivar, PI567343. These findings contribute to the identification of candidate genes for waterlogging tolerance in soybean, which can enhance our understanding of waterlogging tolerance. Full article

Early soybean yield estimation has become a fundamental tool for market policy and food security. Considering a heterogeneous crop, this study investigates the spatial and spectral variability in soybean canopy reflectance to achieve grain yield estimation. Besides allowing crop mapping, remote sensing data also provide spectral evidence that can be used as a priori knowledge to guide sample collection for prediction models. In this context, this study proposes a sampling design method that distributes sample plots based on the spatial and spectral variability in vegetation spectral indices observed in the field. Random forest (RF) and multiple linear regression (MLR) approaches were applied to a set of spectral bands and six vegetation indices to assess their contributions to the soybean yield estimates. Experiments were conducted with a hyperspectral sensor of 25 contiguous spectral bands, ranging from 500 to 900 nm, carried by an unmanned aerial vehicle (UAV) to collect images during the R5 soybean growth stage. The tests showed that spectral indices specially designed from some bands could be adopted instead of using multiple bands with MLR. However, the best result was obtained with RF using spectral bands and the height attribute extracted from the photogrammetric height model. In this case, Pearson’s correlation coefficient was 0.91. The difference between the grain yield productivity estimated with the RF model and the weight collected at harvest was 1.5%, indicating high accuracy for yield prediction. Full article

Crop monitoring with unmanned aerial vehicles (UAVs) has the potential to reduce field monitoring costs while increasing monitoring frequency and improving efficiency. However, the utilization of RGB-based UAV imagery for crop-specific monitoring, especially for sugarcane, remains limited. This work proposes a UAV platform with an RGB camera as a low-cost solution to monitor sugarcane fields, complementing the commonly used multi-spectral methods. This new approach optimizes the RGB vegetation indices for accurate prediction of sugarcane growth, providing many improvements in scalable crop-management methods. The images were captured by a DJI Mavic Pro drone. Four RGB vegetation indices (VIs) (GLI, VARI, GRVI, and MGRVI) and the crop surface model plant height (CSM_PH) were derived from the images. The fractional vegetation cover (FVC) values were compared by image classification. Sugarcane plant height predictions were generated using two machine learning (ML) algorithms—multiple linear regression (MLR) and random forest (RF)—which were compared across five predictor combinations (CSM_PH and four VIs). At the early stage, all VIs showed significantly lower values than later stages (p < 0.05), indicating an initial slow progression of crop growth. MGRVI achieved a classification accuracy of over 94% across all growth phases, outperforming traditional indices. Based on the feature rankings, VARI was the least sensitive parameter, showing the lowest correlation (r < 0.5) and mutual information (MI < 0.4). The results showed that the RF and MLR models provided better predictions for plant height. The best estimation results were observed withthe combination of CSM_PH and GLI utilizing RF model (R² = 0.90, RMSE = 0.37 m, MAE = 0.27 m, and AIC = 21.93). This study revealed that VIs and the CSM_PH derived from RGB images captured by UAVs could be useful in monitoring sugarcane growth to boost crop productivity. Full article

Red-soil sloping farmland in southern China plays a crucial role in the local economy and food production. However, improper tillage practices have resulted in topsoil degradation and deteriorating soil quality. This study investigated changes in soil physico-chemical properties under four tillage methods—cross-slope ridge tillage (RT), down-slope ridge tillage (DT), plastic mulching (PM), and conventional tillage (CT)—on red-soil sloping farmland. The study applied the Soil Management Assessment Framework (SMAF) to assess the influence of these tillage practices on soil quality. Results indicated that PM can increase the total porosity of the soil, reduce soil bulk density, and simultaneously decrease soil surface-water evaporation, significantly improving the soil’s water-retention capacity. RT improved soil aggregate formation and stability, leading to increased macro-aggregate content, mean weight diameter, and soil water-stable aggregate stability rates. PM and RT effectively preserved soil nutrients like total nitrogen and organic matter, although PM lowered soil pH, potentially causing acidification. RT demonstrated the highest soil quality, with PM following. Crop growth positively impacted soil macro-aggregate content and stability, showing continuous improvement in soil structure and quality (p < 0.05). Priority should be given to RT in red-soil sloping farmland, followed by PM and CT, while avoiding DT if possible. This research furnishes valuable scientific substantiation for the selection of optimal tillage practices in the preservation of soil quality on red-soil slopes. Full article

Agriculture is the most important sector that is consuming water resources. In the context of global water scarcity, how to use limited water resources to improve water use efficiency in agriculture or achieve maximum crop yield and fruit quality is of great significance for ensuring food and water security. Optimizing irrigation schedules is an effective measure to improve water use efficiency, where crop models also play an important role. However, there is little research summarizing the optimization of irrigation schedules based on crop models. This study provides a systematic review on how to optimize irrigation schedules based on crop models and simulation–optimization models. When optimizing irrigation schedules based on crop models, the selected models are usually mechanistic agro-hydrological models. Irrigation scenarios and optimization objectives are mainly focused on both crop and water aspects, such as maximizing crop yield, fruit quality, water productivity, and irrigation water productivity. Minimizing crop water consumption and total irrigation amounts serve as optimization objectives, and irrigation quantity, irrigation frequency, and irrigation interval serve as decision variables. In saline areas or low fertilizer utilization areas, the optimization objectives and decision variables also involve some indicators related to salt and nitrogen, such as the maximum desalination rate, minimum salt content, fertilizer utilization efficiency, nitrogen fertilizer productivity, nitrogen fertilizer utilization efficiency, nitrogen leaching rate, which serve as the optimization objectives, and the irrigation water salinity, or fertilization schedules serve as the decision variables. When optimizing irrigation schedules based on simulation–optimization models, the models have mainly been upgraded from water-production function to crop mechanism models. In addition, optimization algorithms have been upgraded from traditional optimization techniques to intelligent optimization algorithms. Decision-making techniques are used to make decisions on optimization results. In addition, the spatial scale for the optimization problem of irrigation schedules was developed from fields to regions, and the time scale was developed from the growth stage, beginning with months, and shortening to ten days, then to a day, and then to an hour. This study also provides a detailed introduction to widely used optimization algorithms, such as genetic algorithms, as well as decision techniques. At the same time, it is proposed that the future should focus on improving crop models and analyzing uncertainty in research on irrigation schedule optimization, which is of great significance for the precise regulation of irrigation schedules. Full article

Preharvest fruit drop is one of the main challenges in apple production as it can lead to extensive crop losses in commercially important cultivars including ‘Honeycrisp’. Plant growth regulators, such as aminoethoxyvinylglicine (AVG) and 1-methylcyclopropene (1-MCP), which hinder ethylene biosynthesis and perception, respectively, can control preharvest fruit drop, but an assessment of their effects in ‘Honeycrisp’ fruit grown under US mid-Atlantic conditions is lacking. In this study, we evaluated the effects of AVG (130 mg a.i. L⁻¹) and 1-MCP (150 mg a.i. L⁻¹) on preharvest fruit drop, ethylene production, fruit physicochemical parameters, skin color, and transcript accumulation of ethylene and anthocyanin-related genes in ‘Honeycrisp’ apples throughout on-the-tree ripening. We showed that both AVG and 1-MCP significantly minimized preharvest fruit drop with respect to the control fruit. Additionally, AVG was the most effective in decreasing ethylene production, downregulating ethylene biosynthesis and perception-related gene expression, and delaying fruit maturity. Nevertheless, AVG negatively impacted apple red skin color and exhibited the lowest expression of anthocyanin-biosynthesis-related genes, only allowing apples to reach the minimum required 50% blush at the last ripening stage. Conversely, 1-MCP-treated fruit displayed an intermediate behavior between AVG-treated and control fruit, decreasing ethylene production rates and the associated gene expression as well as delaying fruit maturity when compared to the control fruit. Remarkably, 1-MCP treatment did not sacrifice red skin color development or anthocyanin-biosynthesis-related gene expression, thus exhibiting > 50% blush one week earlier than AVG. Full article

Environmental factors such as temperature, humidity, light, and CO₂ influence plant growth, and unfavorable environmental conditions cause stress in plants, producing symptoms in their early growth stages. The increasing importance of optimizing crop management strategies has led to a rising demand for the precise evaluation of stress symptoms during early plant growth. Advanced technologies are transforming plant health monitoring through enabling image-based stress analysis. Machine learning (ML) models can effectively identify the important features and morphological changes connected with various stress conditions through the use of large datasets acquired from high-resolution plant images. Therefore, the objective of this study was to develop a method for classifying the early-stage stress symptoms of pepper seedlings and enabling their identification and quantification using image processing and a support vector machine (SVM). Two-week-old pepper seedlings were grown under different temperatures (20, 25, and 30 °C), light intensity levels (50, 250, and 450 µmol m⁻²s⁻¹), and day–night hours (8/16, 10/14, and 16/8) in five controlled plant growth chambers. Images of the seedling canopies were captured daily using a low-cost red, green, and blue (RGB) camera over a two-week period. Eighteen color features, nine texture features using the gray-level co-occurrence matrix (GLCM), and one morphological feature were extracted from each image. A two-way ANOVA and multiple mean comparison (Duncan) analysis were used to determine the statistical significance of the treatment effects. To reduce feature overlap, sequential feature selection (SFS) was applied, and a support vector machine (SVM) was used for stress classification. The SFS method was used to identify the optimal features for the classification model, leading to substantial increases in stress classification accuracy. The SVM model, using these selected features, achieved a classification accuracy of 82% without the SFS and 86% with the SFS. To address overfitting, 5- and 10-fold cross-validation were used, resulting in MAEs of 0.138 and 0.163 for the polynomial kernel, respectively. The SVM model, evaluated with the ROC curve and confusion matrix, achieved a classification accuracy of 85%. This classification approach enables real-time stress monitoring, allowing growers to optimize environmental conditions and enhance seedling growth. Future directions include integrating this system into automated cultivation environments to enable continuous, efficient stress monitoring and response, further improving crop management and productivity. Full article

Rapeseed is the largest self-produced oil crop in China which plays an important role in ensuring the safety of edible oil. However, its current per unit yield is far below Canada and Europe. In this study, selenium fertilizer and other micro fertilizers were sprayed on Xiangzayou 787 at the seedling stage. The results showed that the yield per plant increased 24.3% with sprayed selenium compared to the control (CK). Compared with the CK, the chlorophyll content in leaves at the flowering stage was significantly increased by 20.8%, and the soluble sugar content in the silique wall and seeds at the maturity stage was significantly increased and increased by 62.1% during the budding stage. The functional leaves of Xiangzayou 787 with the sprayed selenium fertilizer and the CK were used as raw materials for absolute quantitative transcriptome sequencing analysis. Accompanied with bioinformatics analysis, six differential genes which affect growth were discovered. The expression level of the protein phosphatase 2C gene in the silique wall was significantly higher than that of the CK. The PP2C78 gene was significantly positively correlated with the chlorophyll and soluble sugar content in leaves and the correlation coefficients were 0.539 and 0.547. According to gene expression levels, yield, and physiological indicators, PP2C78 may be a key functional gene affecting rapeseed yield. In this study, selenium fertilizer was found to be an excellent foliar fertilizer for rapeseed; the PP2C78 gene may be helpful for analyzing the yield increasing mechanism and used as a reference for screening new foliar fertilizers. Full article