Search

This study aimed to reveal the effects of arbuscular mycorrhizae and K-humate on some of the morphological characteristics and growth of Russian olive (RO; Elaeagnus angustifolia L.) and black locust (BL; Robinia pseudoacacia L.). The indigenous mycorrhizal spores (Claroideoglomus claroideum, Claroideoglomus etunicatum, Claroideoglomus luteum, and Funneliformis mosseae) collected from rhizospheres of RO and BL trees in afforestation sites located in Central Anatolia. In addition, commercial mycorrhizal mixture and K-humate were used as treatments. Five treatments (1—indigenous mycorrhizal fungi, 2—K-humate, 3—indigenous mycorrhizal fungi + K-humate, 4—commercial mycorrhizal fungi, and 5—control) were assigned in a completely randomized design for both tree species. Four months after the treatments, inoculation rate, above-ground seedling height, fresh and dry weight, root collar diameter, length, fresh, and dry weight, leaf area index, shot-to-root dry weight ratio, seedling height to root collar diameter ratio, and Dickson quality index were determined. Plant and soil analyses were carried out to determine the effects of treatments on plant and soil nutrition. Indigenous arbuscular mycorrhizal fungi and K-humate combinations had positive effects on the morphological characteristics and nutritional status of the seedlings. The indigenous mycorrhizal and K-humate interaction showed the most pronounced effects on RO growth and nutrition. [ABSTRACT FROM AUTHOR]

Grape is a staple crop in many parts of Iran which has shown moderate sensitivity to salinity stress. Water and soil salinity is one of the major environmental stresses that strongly affect the production of agricultural products, including grapes. To overcome the harmful effects of salinity, different methods and materials are used, one of which is the use of nitric oxide. In order to explore the impact of nitric oxide on the mitigation of the negative effects of salinity stress on four grape cultivars and rootstocks ('Bidaneh Sefid' (Sultana) and 'Yaghouti' cultivars, and 140Ru and 1103 P rootstocks), a pot experiment was performed in a factorial based on a randomized complete block design with three replications. Plants were subjected to three sodium chloride (NaCl) levels of 0 (control), 25, and 50 mM and three levels of sodium nitroprusside (SNP) of 0 (control), 0.5, and 1 mM. Results indicated that increasing SNP concentration caused an increase in growth indices such as leaf area, shoot and root length, fresh and dry weights of leaves, shoots, and roots, and leaf relative water content (RWC). Furthermore, salinity decreased the concentrations of potassium (K+), calcium (Ca2+), magnesium (Mg2+), and iron (Fe2+) in leaves, while increased the amount of sodium (Na+) and chlorine (Clˉ) as well as the electrolyte leakage (EL). In addition, SNP at 0.5 and 1 mM could increase the growth efficiency and RWC as well as the elements such as K+ and Mg2+ while decreased the absorption of Na+ and Clˉ as well as the EL in plants under salinity. According to the obtained results, SNP at both concentrations (0.5 and 1 mM) had a pronounced effect on reducing the negative effect of salinity in the evaluated grape rootstocks and cultivars. In general, the positive effects of SNP on 'Yaghouti' and 'Bidaneh Sefid' cultivars were higher than those on 140Ru and 1103 P rootstocks. [ABSTRACT FROM AUTHOR]

BACKGROUND: The simultaneous prediction of yield and maturity date has an important impact on ensuring food security. However, few studies have focused on simultaneous prediction of yield and maturity date for wheat–maize in the North China Plain (NCP). In this study, we developed the prediction model of maturity date and yield (PMMY) for wheat–maize using multi‐source satellite images, an Agricultural Production Systems sIMulator (APSIM) model and a random forest (RF) algorithm. RESULTS: The results showed that the PMMY model using peak leaf area index (LAI) and accumulated evapotranspiration (ET) has the optimal performance in the prediction of maturity date and yield. The accuracy of the PMMY model using peak LAI and accumulated ET was higher than that of the PMMY model using only peak LAI or accumulated ET. In a single year, the PMMY model had good performance in the prediction of maturity date and yield. The latitude variation in spatial distribution of maturity date for WM was obvious. The spatial heterogeneity for yield of wheat–maize was not prominent. Compared with 2001–2005, the maturity date of the two crops in 2016–2020 advanced 1–2 days, while yield increased 659–706 kg ha−1. The increase in minimum temperature was the main meteorological factor for advance in the maturity date for wheat–maize. Precipitation was mainly positively correlated with maize yield, while the increase in minimum temperature and solar radiation was crucial to the increase in yield. CONCLUSION: The simultaneous prediction of yield and maturity can be used to guide agricultural production and ensure food security. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: The integration of inorganic and organic fertilizers is increasingly being recommended to address the demand for sustainable cotton cultivation and to mitigate the ecological impacts of reliance on inorganic fertilizers. However, the nuanced effects of this combined fertilization approach on soil quality, cotton growth, yield, and their interaction mechanisms, remain unclear. METHOD: To elucidate this, a 2‐year field trial (2022–2023) was conducted, incorporating five fertilization treatments: low inorganic fertilizer (BI1), high inorganic fertilizer (BI2), organic fertilizer (BO), combined low inorganic and organic fertilizer (BIO1), and combined high inorganic and organic fertilizer (BIO2). This study aimed to evaluate the influence of these treatments on soil quality, cotton growth, and yield. RESULTS: The results indicate that the BO treatment significantly enhanced plant height growth rate, and BIO1 treatment increased leaf area index and dry matter accumulation growth rate. Critical soil parameters such as alkali‐hydrolyzed nitrogen and available potassium emerged as pivotal determinants of soil quality over the trial period, corresponding to soil quality index (SQI) values of 0.482 and 0.478, and yields of 7506.19 kg ha−1 and 6788.02 kg ha−1, respectively. Water productivity reached optimum levels at SQI values of 0.461 and 0.462, with corresponding efficiencies of 13.31 kg (ha mm)−1 and 12.16 kg (ha mm)−1. Partial least squares path modeling revealed that integrating organic fertilizer with reduced inorganic fertilizer usage significantly boosts cotton yield by enhancing soil quality (path coefficient: 0.842). CONCLUSION: In conclusion, this integrated fertilization strategy not only improves soil health but also increases agricultural productivity. It presents a promising approach for optimizing crop yields while fostering sustainable agricultural practices. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The ability to access physiologically driven signals, such as surface temperature, photochemical reflectance index (PRI), and sun-induced chlorophyll fluorescence (SIF), through remote sensing (RS) are exciting developments for vegetation studies. Accessing this ecophysiological information requires considering processes operating at scales from the top-of-the-canopy to the photosystems, adding complexity compared to reflectance index-based approaches. To investigate the maturity and knowledge of the growing RS community in this area, COST Action CA17134 SENSECO organized a Spatial Scaling Challenge (SSC). Challenge participants were asked to retrieve four key ecophysiological variables for a field each of maize and wheat from a simulated field campaign: leaf area index (LAI), leaf chlorophyll content (Cab), maximum carboxylation rate (Vcmax,25), and non-photochemical quenching (NPQ). The simulated campaign data included hyperspectral optical, thermal and SIF imagery, together with ground sampling of the four variables. Non-parametric methods that combined multiple spectral domains and field measurements were used most often, thereby indirectly performing the top-of-the-canopy to photosystem scaling. LAI and Cab were reliably retrieved in most cases, whereas Vcmax,25 and NPQ were less accurately estimated and demanded information ancillary to RS imagery. The factors considered least by participants were the biophysical and physiological canopy vertical profiles, the spatial mismatch between RS sensors, the temporal mismatch between field sampling and RS acquisition, and measurement uncertainty. Furthermore, few participants developed NPQ maps into stress maps or provided a deeper analysis of their parameter retrievals. The SSC shows that, despite advances in statistical and physically based models, the vegetation RS community should improve how field and RS data are integrated and scaled in space and time. We expect this work will guide newcomers and support robust advances in this research field. [ABSTRACT FROM AUTHOR]

The neglected use of micronutrient greatly hampered the productivity of oilseed crop. Thus, to determine the optimal dose of S and B on toria under rice-fallow system, an experiment was carried out with five doses of S i.e. 0, 10, 20, 30 and 40 kg/ha and 3 doses of B i.e. 0, 1 and 2 kg/ha for two consecutive years (2018–19 and 2019–20). The plant growth parameters, i.e. plant height, dry matter accumulation, leaf area index and crop growth rate, and yield attributing parameters, i.e. siliqua/plant and seeds/siliqua, were greatly enhanced through application of S @ 30 kg/ha as well as B@ 2 kg/ha. The higher seed yield (10.11q/ha), stover yield (22.00q/ha) and oil yield (3.86 q/ha) were registered with the application of S @ 30 kg/ha + B @ 2 kg/ha, but it was at par with S@ 30 kg/ha + B @ 1 kg/ha. The combined application of S@ 30 kg/ha + B @ 2 kg/ha also noted higher plant nutrient uptake of N, P, K including S and B closely followed by S@ 30 kg/ha + B @ 1 kg/ha. But in terms of economics, conjugate application of S@ 30 kg/ha + B @1 kg/ha showed maximum monetary benefit with highest B:C (2.24). The pearson correlation indicated strong and positive correlation of seed yield with growth and yield parameters as well as nutrient uptake by plants. Regression analysis revealed that each one unit increase in plant dry matter accumulation showed an increase in seed yield by 4.56 unit and each one increase uptake in N, P, K, S and B showed an increase in seed yield by 0.19, 0.63,0.40,0.55 and 0.05, respectively. Henceforth, considering all the factors the combination of S@ 30 kg/ha and B@1 kg/ha may be recommended under the late sown toria in rice-fallow system. [ABSTRACT FROM AUTHOR]

Timely estimations of leaf chlorophyll content (LCC) and leaf area index (LAI) can provide critical information for potato field management. We employed a hybrid method that integrated machine learning with a radiative transfer model to estimate potato growth parameters. A look-up table was generated using the PROSAIL model, which was used as an unlabelled sample set. Measurements were taken from a potato field, and the data were labelled according to growth period and variety. Then, training samples for potato LCC, LAI, and canopy chlorophyll content (CCC) were selected from the simulated unlabelled sample set using the Euclidean distance-based diversity algorithm based on different labelled data sets. The training sample size required to accurately estimate the parameters varied considerably depending on the parameter, variety, and growth period, despite using the same labelled data set. Moreover, our results indicate that the growth period has a substantial impact on model accuracy and needs to be considered when constructing the labelled data set. The study results indicate that the hybrid method combined with the radiative transfer model and active learning can effectively select informative training samples from a data pool and improve the accuracy of potato parameter estimation, which provides a valid tool for accurately monitoring crop growth and growth health. [ABSTRACT FROM AUTHOR]

Spectral and texture features play important roles in plantation leaf area index (LAI) estimation, and their combination may enhance LAI inversion accuracy. Furthermore, research on the impact of different machine learning (ML) models on their hyperparameter combinations and splitting ratios remains challenging. In our study, experiments based on spectral and textural features of GF-6 WFV data were conducted on Eucalyptus grandis plantation forests in Huangmian Town, Guangxi, China. ML methods such as multiple stepwise regression (MSR), random forest (RF), back-propagation neural network (BPNN), and support vector regression (SVR) were mainly utilized to perform model hyper-parameter tuning and split-ratio analysis in order to estimate the LAI. The results of the study showed that spectral and gray level co-occurrence matrix (GLCM) texture features were very sensitive to changes in Eucalyptus grandis LAI. The accuracy of combining the two was 10% higher than when they were not combined. Furthermore, it was found that the nonlinear methods (RF, BPNN, and SVR) outperformed the linear method (MSR), with the average 2 Rmax of the nonlinear model being 26% higher than that of the linear model, and the RMSE value being 29% lower than that of the linear model. In addition, by analyzing different combinations of features, model hyperparameter fine-tuning, and splitting ratios in the nonlinear model, it was found that the splitting ratios of different combinations of model hyperparameters have a great impact on the accuracy of the model. A total of 12 out of 21 data sets showed high accuracy and stability at a split ratio of 8.5:1.5 (ratio of 0.85), with the best-performing RF model differing from the lowest by 91% for 2 max R and 39% for 2 Rstd. Combining spectral and texture features provides highly accurate inversion data. Model hyper-parameter fine-tuning and segmental scale tuning can facilitate the application of inversion data to fully utilize the optimal performance of the ML model. [ABSTRACT FROM AUTHOR]

Water scarcity, over-fertilization, and improper crop management practices severely limit the sustainable cultivation of licorice (Glycyrrhiza uralensis Fisch) in the arid regions of Xinjiang. To elucidate the impacts of integrated water and fertilizer management on the growth characteristics and bioactive components (glycyrrhizic acid and liquiritin) of four-year-old licorice plants, a comprehensive four-year field experiment was conducted from 2019 to 2022. The experiment included four irrigation levels (W1: 2500 m³/ha, W2: 4000 m³/ha, W3: 5500 m³/ha, W4: 7000 m³/ha) and four fertilization rates (F1: 305 kg/ha, F2: 610 kg/ha, F3: 915 kg/ha, F4: 1220 kg/ha), following a completely randomized design. Results indicated that both irrigation and fertilization significantly influenced plant height, root length, root weight, root diameter, leaf area index, and root-to-shoot ratio. The optimal growth characteristics were observed under the W2F2 treatment. The contents of glycyrrhizic acid and liquiritin varied significantly among different water and fertilizer treatments, with the highest levels observed under the W2F2 treatment. Excessive irrigation (W4) and over-fertilization (F4) led to a decrease in these bioactive components. A comprehensive evaluation of the growth characteristics and bioactive components revealed that the ideal irrigation and fertilization parameters were 4000 m³/ha and 610 kg/ha, respectively. These parameters optimized plant development and bioactive component accumulation while ensuring efficient resource use. This study provides scientific evidence for optimizing irrigation and fertilization strategies to enhance licorice yield in arid regions, thereby supporting sustainable agricultural practices and improving economic benefits. [ABSTRACT FROM AUTHOR]

Forest biomass estimates are required for many applications, including accounting for the role of forests in the global carbon cycle, supporting sustainable forest management and making informed decisions. For all these applications and others, accurate and reliable forest biomass estimates are required. This study evaluated the potential of Sentinel-2 data for predicting and mapping aboveground biomass (AGB) of a dry Afromontane Forest in Tigray, Northern Ethiopia. Multiple linear regression was employed to model the relationship between AGB and Sentinel-2-derived spectral variables. The evaluation criteria for best-fit model selection were based on the coefficient of determination (R2), root mean square error (RMSE, %), and bias (Bias, %). All the spectral variables evaluated here were significantly correlated with AGB (p < 0.01). The model that includes a fraction of photosynthetically active radiation (FAPAR), leaf area index (LAI), Band 2 and Band 3 as predictor variables provided the best predictive performance for AGB in the study area (R2 = 0.38, RMSE = 13.62 Mg ha−1 and Bias = -3.10 Mg ha−1). The predicted AGB of the study area ranges from 0.1 to 141.8 Mg ha−1, with a mean value of 12.4 Mg ha−1. The results from this study suggested that Sentinel-2 data can be potentially applied for estimating and mapping AGB in dry Afromontane forests. [ABSTRACT FROM AUTHOR]

A three-year multi-location trial on the farmer's field was conducted in West Bengal, India to evaluate the impact of integrated nutrient management (INM) on the performance of kharif rice cv. Swarna Sub 1. The experiment employed a randomized block design (RBD) across eleven farmers' fields with similar soil properties, texture, topography, and historical fertilizer application patterns. Three treatments were tested: T1 – common practice of applying N:P:K @ 55:32:27 kg ha−1, T2 – soil test-based application (STA) of ∼75% recommended N dose through chemical fertilizer + ∼ 25% through Azolla, and T3 – soil test-based application of ∼ 75% recommended N dose through chemical fertilizer + 20% N from vermi-compost + 5% N through Azolla. The result of the experiment revealed that the T3 exhibited significant improvements in plant height, leaf area index (LAI), effective tillers meter−2, number of panicles plant−1, and 1000-grain weight compared to T2 and T1. Among the different nutrient management practices, the T3 recorded maximum rice grain productivity than the T1, and T2, Further, the benefit-cost ratio was noticed significantly higher under T3 (1.29) followed by T2 (1.27) and T1 (1.23). The soil organic carbon (SOC), available nutrient was enhanced by the soil test-based application of ∼75% recommended N dose through chemical fertilizer + 20% N from vermicompost + 5% N through Azolla. Thus, our study indicated that the balanced nutrient application through combined sources (T3) could improve crop growth; and sustain rice productivity, besides enhancing the farm income, and soil different soil properties. [ABSTRACT FROM AUTHOR]

Silicon (Si) is a versatile nutrient that plays an instrumental role in mitigating biotic and abiotic stresses besides improving growth and yield of graminaceous crops. We hypothesized that application Si would significantly improve productivity and resilience of wheat. Hence, the objectives were a) to assess the impact of Si application on wheat growth, productivity, nutrient uptake, water use efficiency, and b) to determine the potentiality of Si in mitigating lodging and aphid density. Therefore, we conducted a field experiment with five levels of Si (0, 2, 4, 6, and 8 g Si liter−1) at three growth stages (crown root initiation, tillering, and jointing stage) using a factorial randomized block design replicated three times. The results showed that increasing Si doses positively influenced plant height, dry matter accumulation (DMA), leaf area index (LAI), yield, and nutrient uptake. The highest grain and straw yield were observed with 8 g Si liter−1, followed by 6 g Si liter−1, while the control had the lowest yields. With 8 g Si liter−1, grain yield, straw yield, and Si uptake increased by 10.5%, 13.5%, and 26.3%, respectively, compared to the control. Additionally, Si application at 8 g Si liter−1 significantly reduced the density of S. avenae (aphids) by 81.4% and lodging by 75.1% compared to the control. Overall, the study demonstrated that increasing Si doses enhanced various growth and yield parameters, with 8 g Si liter−1 and 6 g Si liter−1 showing superior results. Among the growth stages, foliar application of Si during the tillering stage exhibited better performance in terms of growth, yield, and nutrient uptake in wheat. Therefore, the study concludes that Si fertilization at a rate of 8 g Si liter−1 during the tillering stage can effectively improve growth, productivity, and nutrient uptake in wheat in the southern region of Rajasthan. [ABSTRACT FROM AUTHOR]

Forest ecosystems play a decisive role in the global climatic condition, as well as, provides a wide range of societal benefits, including fuel-wood, tourism, and ecosystem services are considered as one of the major sources of livelihood for the local people in the upper Blue Nile Basin. Therefore, rapid and accurate estimation of forest biomass is crucial for greatly reducing the uncertainty in carbon stock assessments, and for designing strategic forest management plans. Because, above-ground biomass (AGB) estimation is important in determining the management, environmental, and economic roles of forests in the Blue Nile basin. The study was aimed at estimating above-ground biomass in the Upper Blue Nile Basin forests by integrating field-measured data with predictors from Sentinel-2 image. The relationship between measured AGB and sentinel-2 derived vegetation indices and biophysical parameters showed a good correlation result (r value ranging from 0.67 to 0.74). A stepwise regression analysis was carried out in order to develop AGB estimation model by identifying the most important variable. The result demonstrated that, green normalized difference vegetation index, leaf area index, fraction of absorbed photosynthetic active radiation and fractional vegetation cover achieved good performance in predicting AGB with R2 value > 0.5. AGB was estimated with a coefficient of determination (R2) of 0.59 adjusted R2 of 0.618 and root mean square error of (RMSE) 38.36 t/ha in comparison to field observations. The maximum AGB value of 268.32 t/ha was estimated in the Alemsaga natural forest, which is a highly protected dense forest stand from any entrance and disturbance. Generally, integrating field data with optical remote sensing data provides more reliable result for AGB estimation. Moreover, it is also recommended to employ RADAR and LiDAR remote sensing data products together in order to attain more precise estimate results of AGB with great potential for forest resource monitoring and management. [ABSTRACT FROM AUTHOR]

Introduction: Long-term application of excessive nitrogen (N) not only leads to low N use efficiency (NUE) but also exacerbates the risk of environmental pollution due to N losses. Substituting partial chemical N with organic fertilizer (SP) is an environmentally friendly and sustainable fertilization practice. However, the appropriate rate of SP in rainfed maize cropping systems in semi-arid regions of China is unknown. Methods: Therefore, we conducted a field experiment between 2021 and 2022 in a semi-arid region of Northern China to investigate the effects of SP on maize growth, carbon and N metabolism (C/NM), and NUE. The following treatments were used in the experiment: no N application (CK), 100% chemical N (SP0, 210 kg N ha-1), and SP substituting 15% (SP1), 30% (SP2), 45% (SP3), and 60% (SP4) of the chemical N. The relationship between these indicators and grain yield (GY) was explored using the Mantel test and structural equation modeling (SEM). Results and discussion: The results found that the SP1 and SP2 treatments improved the assimilates production capacity of the canopy by increasing the leaf area index, total chlorophyll content, and net photosynthetic rate, improving dry matter accumulation (DMA) by 6.2%-10.6%, compared to the SP0 treatment. SP1 and SP2 treatments increased total soluble sugars, starch, free amino acids, and soluble protein contents in ear leaves via increasing the enzymatic reactions related to C/NM in ear leaves during the reproductive growth stage compared with SP0 treatment. The highest plant nitrogen uptake (PNU) and nitrogen recovery efficiency were obtained under the SP2 treatment, and the GY and nitrogen agronomic efficiency were higher than the SP0 treatment by 9.2% and 27.8%. However, SP3 and SP4 treatments reduced DMA and GY by inhibiting C/NM in ear leaves compared to SP0 treatment. Mantel test and SEM results revealed that SP treatments indirectly increased GY and PNU by directly positively regulating C/NM in maize ear leaves. Therefore, in the semi-arid regions, substituting 30% of the chemical N with SP could be considered. This fertilizer regime may avoid GY reduction and improve NUE. This study provides new insights into sustainable cultivation pathways for maize in semi-arid regions. [ABSTRACT FROM AUTHOR]

Cold stress is a critical factor affecting rice production worldwide. The application of cold-resistant agents may improve the cold resistance and yield of crops. To screen for suitable cold-resistant agents for machine-transplanted early rice, the effects of uniconazole, abscisic acid, and zinc-amino acids chelate and their spraying times (seed soaking stage, one leaf and one heart stage, two leaves and one heart stage, 7 days before the transplanting stage, and regreening stage) on the yield and cold resistance of machine-transplanted early rice were investigated. Moreover, the application method (spraying amount: 750 and 1125 g ha-1; spraying time: 7 days before the transplanting stage, transplanting stage, regreening stage, and transplanting stage and regreening stage) for the most suitable cold-resistant agent was optimized. The zinc-amino acids chelate was better than the other two cold-resistant agents for promoting rice tillering and increasing the leaf area index, dry matter weight, antioxidant enzyme activities (CAT, SOD, POD) and yield (i.e., 9.22% and 7.14% higher than uniconazole and abscisic acid, respectively), especially when it was applied in the regreening stage. The examination of spraying amounts and times indicated that the zinc-amino acids chelate dosage had no significant effect on the yield and cold resistance of early rice. However, the rice yield and antioxidant enzyme activities were highest when samples were sprayed once in the transplanting stage and the regreening stage. On the basis of the study results, 750 g ha-1 zincamino acids chelate applications in the transplanting and regreening stages of machine-transplanted early rice plants may be ideal for increasing cold stress resistance and yield. [ABSTRACT FROM AUTHOR]

Evaporative fraction (EF) is a useful measure for quantifying land surface energy partitioning processes and determining evaporative regimes; however, its influencing factors remain highly uncertain. Here, global data sets were compiled to disentangle the effects of environmental variables on EF variations along climate and land surface gradients. We found that (a) at annual timescales, ecosystem‐level EF could be expressed as a power law function of aridity index. The relationships of mean annual soil water content (SWC) and leaf area index (LAI) with mean annual EF resembled the traditional evaporative regime theory; (b) at daily timescales, the boosted regression tree method quantitatively revealed that the impacts of environmental variables (including meteorological variables) on EF showed equal importance, especially at humid sites, primarily due to the different response direction and magnitude of latent heat (LE) and sensible heat (H) fluxes to environmental changes. Particularly, the contrasting responses of LE (positive) and H (negative) to SWC, LAI, and relative humidity enhanced the positive effects of those influencing variables on EF; whereas, the correlations between EF and energy‐related factors (i.e., net radiation‐Rn and air temperature‐Ta) deteriorated as both LE and H showed positive response patterns to those variables; (c) meteorological factors were also found to have nonlinear effects on daily EF, further modified by climatic conditions. Rn near 150 W/m2 and Ta near 15°C appeared to be important energy‐partitioning thresholds at drier and humid sites, respectively. Moreover, changing interactions among environmental variables with climates were demonstrated to be important for better explaining EF variations. Plain Language Summary: Evaporative fraction (EF; defined as latent heat flux‐LE divided by the sum of LE and sensible heat flux‐H) can vary under different climatic and land surface conditions, but its influencing factors remain poorly understood. To this end, we explored the effects of environmental variables on annual and daily EF variations at different sites around the globe. We found that mean annual EF decreased with increasing aridity index and increased with mean annual soil water content and leaf area index. By comparison, the boosted regression tree method quantitatively showed that environmental variables exerted equally important roles in regulating daily EF, especially at humid sites. The complex interplays of daily EF with environmental variables were mainly due to the different responses of LE and H to surrounding environments and the strong nonlinear and interactive effects of environmental variables. These results are important for understanding the driving mechanisms of EF and land surface energy partitioning processes along climate and land surface gradients. Key Points: Environmental variables exerted equally important roles in regulating daily evaporative fraction, especially at humid sitesSynchronous responses of latent and sensible heat to surroundings determined how environmental variables affected evaporative fractionEnvironmental factors had strong nonlinear and interactive effects on daily evaporative fraction, which was further modified by climates [ABSTRACT FROM AUTHOR]

Carbon Dots (CDs) were synthesized from peanut shells (PNS) through pyrolysis and characterized using FTIR, XRD, HRTEM and BET analysis revealing an average size of 2–5 nm with amorphous nature. Synthesized PNS-CDs was employed both as priming and foliar agent for enhancing seed quality and crop productivity in blackgram (Vigna mungo L. Hepper). Different concentrations ranging from 50, 100, 200, 300, 400, 500 and 1000 ppm was used for seed priming and 5, 10, 15, 20, 25, 50, 75 and 100 ppm were given as foliar spray on 30th and 45th days after sowing (DAS). On accounting the best seed priming and foliar spray concentrations, field trial was conducted to validate the optimistic effect of PNS-CDs on blackgram crop productivity. Results revealed that priming with 200 ppm for 3 h exhibited maximum seed imbibition (54%), germination (88%) and vigour index (3165). Whereas, foliar spray with 50 ppm expressed significant improvement in leaf area index (2.6), total chlorophyll (2.70 mg/g), total soluble protein (71 mg/g), Number of nodules/plant (138), seed yield/plant (8.7 g) and 100 seed weight (5 g). The impact of PNS-CDs treatments resulted in increased photosynthetic rate (12.45 µmol CO2 m−2s−1), transpiration rate (3.13 mmol H2O/m−2s−1), stomatal conductance (0.55 mol H2O/m−2s−1) and internal leaf CO2 concentration (652 µmol CO2 m−2s−1) which ultimately enhanced the photosynthetic efficiency of plants. It has also exhibited a promising effect on the resultant seed in which the combination seed priming (200 ppm) followed by foliar spray (50 ppm) recorded maximum 100 seed weight (4.19 g), germination (97%) and vigour index (3019). Thus, this study highlights the promising role of PNS-CDs as a sustainable and effective agricultural nanomaterial, offering a novel approach to utilize the agricultural waste and also to enhance the crop productivity through advanced non-chemical approach. [ABSTRACT FROM AUTHOR]

AbstractIndigenous fermented liquid formulations (Panchagavya, Jeevamrut and Amritpani) are rich sources of beneficial microflora and fauna, vitamins, minerals and growth-promoting hormones. Small and marginal farmers of North-West Himalayan region popularly grow sweet pepper as commercial crop. Poultry manure and vermicompost are two cheap nutrient sources, popular among these growers. So, inorder to investigate the combined effect of these organic inputs, two-year field experiment with seven treatments and three replications; with randomized block design; comprised of 100, 90, 80, 70, 60, 50, 40 per cent recommended dose of nutrients (RDN) which was applied through vermicompost and poultry manure along with Panchagavya (5%), Jeevamrut (5%), Amritpani (5%) and plant growth promoting rhizobacteria which was isolated from experimental location, applied to all treatments, except T1; was carried out to study the response of these inputs on quality parameters of sweet pepper. Results revealed that treatment T2 that comprised of 90% RDN including Panchagavya, Jeevamrut, Amritpani and PGPR led to noticeably improvement in growth and quality parameters viz. chlorophyll-a (0.52 mg g−1), chlorophyll-b (0.73 mg g−1), ascorbic acid (29.47 mg 100 g−1), protein content (28.90%), capsaicin content (0.23 mg g−1), number of primary branches (8.20), average fruit weight (62 g), fruit yield (290.58 quintals ha−1), number of flowers per plant (24.97), leaf area index (4.73) and shelf life (6.03 days). Hence, it is concluded that the combined use of these inputs has the potential to enhance growth and quality characteristics of sweet pepper in achieving sustainable crop productivity. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is a crucial vegetation parameter that characterizes leaf sparsity and canopy structure, and the study of the spatial distribution pattern of the forest LAI and its environmental response can help to reveal the adaptive capacity of forest vegetation to climate change in semiarid areas. In this paper, a remote sensing inversion model of the LAI, which pertains to the forest ecosystem of Xinglong Mountain in the transition zone between the Qinghai‒Tibet Plateau and Loess Plateau, was established by combining an optical instrumentation method, a remote sensing inversion method, and a generalized additive model (GAM). The results showed that (1) the Meris terrestrial chlorophyll index (MTCI) linear regression model provided the greatest explanatory power for the LAI in the Xinglong Mountain forest, with R2 = 0.88 and RMSE = 0.32. (2) The LAI was influenced mainly by the altitude, slope, profile curvature, aspect, planform curvature, temperature, precipitation, and evapotranspiration. According to the single-factor GAM, altitude (R2 = 0.43) explained most of the total variation in the LAI, followed by precipitation (R2 = 0.36). According to the multifactor GAM, the above influencing factors could explain 84.2% of the total variation in the LAI, which was significant (P < 0.001). (3) Interaction analysis revealed that the LAI was significantly influenced by the interaction between topographic and meteorological factors (P < 0.001). It was revealed that the topography of Xinglong Mountain is fragmented, the vertical band spectrum of vegetation is notable, and the forest LAI exhibits high spatial heterogeneity under the interaction between topographic and meteorological factors, reflecting the environmental response mechanism of vegetation growth in forest ecosystems in ecological transition zones. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System Reflectometry (GNSS-R) is a remote sensing technique that can be regarded as a bistatic radar system. GNSS-R uses GNSS signals as signal sources and obtains the Earth's surface environmental parameters, such as soil moisture (SM), by receiving the L-band microwave signal reflected from the Earth's surface. However, surface vegetation could be one of the main factors influencing the accuracy of GNSS-R land applications since plants, including branches and leaves, attenuate GNSS signals. Additionally, the evaluation of signal attenuation caused by the plant canopy is quite difficult. In this paper, we study the attenuations of received L1- and L2-band GPS signals to the vegetation leaf area index (LAI) for different types of plants. The relationship between the attenuation of the GPS signal-to-noise ratio (SNR) (both above and below the canopy) and the LAI is established through field experiments. The results show that the mean SNR received in the L2 band is lower than that in the L1 band for each satellite but with a larger standard deviation (SD). The sensitivities of L1- and L2-band signals to the LAI are revealed, revealing greater sensitivity and a relatively good Pearson correlation coefficient (R) for lower elevation angles and vegetation biomass. In addition, the sensitivity and R of L2-band signals to the LAI are lower than those of L1-band signals. This study is significantly valuable for improving the quantitative representation of error estimates for GNSS-R SM retrieval. The established model can be employed in GNSS land applications and aid in solving signal surface-scattering problems in which accurate signal estimates are important. [ABSTRACT FROM AUTHOR]

Remote sensing and process-coupled ecological models are widely used for the simulation of GPP, which plays a key role in estimating and monitoring terrestrial ecosystem productivity. However, most such models do not differentiate the C3 and C4 photosynthetic pathways and neglect the effect of nitrogen content on V max and J max , leading to considerable bias in the estimation of gross primary productivity (GPP). Here, we developed a model driven by the leaf area index, climate, and atmospheric CO 2 concentration to estimate global GPP with a spatial resolution of 0.1° and a temporal interval of 1 day from 2000 to 2022. We validated our model with ground-based GPP measurements at 128 flux tower sites, which yielded an accuracy of 72.3%. We found that the global GPP ranged from 116.4 PgC year − 1 to 133.94 PgC year − 1 from 2000 to 2022, with an average of 125.93 PgC year − 1 . We also found that the global GPP showed an increasing trend of 0.548 PgC year − 1 during the study period. Further analyses using the structure equation model showed that atmospheric CO 2 concentration and air temperature were the main drivers of the global GPP changes, total associations of 0.853 and 0.75, respectively, while precipitation represented a minor but negative contribution to global GPP. [ABSTRACT FROM AUTHOR]

Radiative transfer models (RTMs) are often used to retrieve biophysical parameters from earth observation data. RTMs with multi-temporal and realistic forest representations enable radiative transfer (RT) modeling for real-world dynamic processes. To achieve more realistic RT modeling for dynamic forest processes, this study presents the 3D-explicit reconstruction of a typical temperate deciduous forest in 2015 and 2022. We demonstrate for the first time the potential use of bitemporal 3D-explicit RT modeling from terrestrial laser scanning on the forward modeling and quantitative interpretation of: (1) remote sensing (RS) observations of leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and canopy light extinction, and (2) the impact of canopy gap dynamics on light availability of explicit locations. Results showed that, compared to the 2015 scene, the hemispherical-directional reflectance factor (HDRF) of the 2022 forest scene relatively decreased by 3.8% and the leaf FAPAR relatively increased by 5.4%. At explicit locations where canopy gaps significantly changed between the 2015 scene and the 2022 scene, only under diffuse light did the branch damage and closing gap significantly impact ground light availability. This study provides the first bitemporal RT comparison based on the 3D RT modeling, which uses one of the most realistic bitemporal forest scenes as the structural input. This bitemporal 3D-explicit forest RT modeling allows spatially explicit modeling over time under fully controlled experimental conditions in one of the most realistic virtual environments, thus delivering a powerful tool for studying canopy light regimes as impacted by dynamics in forest structure and developing RS inversion schemes on forest structural changes. [ABSTRACT FROM AUTHOR]

The Leaf Area Index (LAI) is a critical parameter that sheds light on the composition and function of forest ecosystems. Its efficient and rapid measurement is essential for simulating and estimating ecological activities such as vegetation productivity, water cycle, and carbon balance. In this study, we propose to combine high-resolution GF-6 2 m satellite images with the LESS three-dimensional RTM and employ different machine learning algorithms, including Random Forest, BP Neural Network, and XGBoost, to achieve LAI inversion for forest stands. By reconstructing real forest stand scenarios in the LESS model, we simulated reflectance data in blue, green, red, and near-infrared bands, as well as LAI data, and fused some real data as inputs to train the machine learning models. Subsequently, we used the remaining measured LAI data for validation and prediction to achieve LAI inversion. Among the three machine learning algorithms, Random Forest gave the highest performance, with an R2 of 0.6164 and an RMSE of 0.4109, while the BP Neural Network performed inefficiently (R2 = 0.4022, RMSE = 0.5407). Therefore, we ultimately employed the Random Forest algorithm to perform LAI inversion and generated LAI inversion spatial distribution maps, achieving an innovative, efficient, and reliable method for forest stand LAI inversion. [ABSTRACT FROM AUTHOR]

In recent decades, drought has intensified along with continuous global warming, significantly impacting terrestrial vegetation. High atmospheric water demand, indicated by vapor pressure deficit (VPD), and insufficient soil moisture (SM) are considered the primary factors causing drought stress in vegetation. However, the influences of VPD and SM on the autumn phenology are still unknown. Using satellite observations and meteorological data, we examined the impacts of VPD and SM on the end of the growing season (EOS) across the Northern Hemisphere (>30°N) from 1982 to 2022. We found that VPD and SM were as important as temperature, precipitation, and radiation in controlling the variations in the EOS. Moreover, the EOS was predominantly influenced by VPD or SM in more than one-third (33.8%) of the study area. In particular, a ridge regression analysis indicated that the EOS was more sensitive to VPD than to SM and the other climatic factors, with 25% of the pixels showing the highest sensitivity to VPD. In addition, the effects of VPD and SM on the EOS varied among biome types and climate zones. VPD significantly advanced the EOS in 25.8% of temperate grasslands, while SM had the greatest impact on advancing the EOS in 17.7% of temperate coniferous forests. Additionally, 27.7% of the midlatitude steppe (BSk) exhibited a significant negative correlation between VPD and the EOS, while 19.4% of the marine west coast climate (Cfb) showed a positive correlation between SM and the EOS. We also demonstrated that the correlation between VPD and the EOS was linearly affected by VPD and the leaf area index, while the correlation between SM and the EOS was affected by SM, precipitation, and the leaf area index. Our study highlights the importance of VPD and SM in regulating autumn phenology and enhances our understanding of terrestrial ecosystem responses to climate change. [ABSTRACT FROM AUTHOR]

Nitrogen (N) is one of the most important fertilizers in agriculture because it promotes the growth of plants and the uptake of other plant nutrients. This nutrient plays a significant role in determining the yield and nutrient composition of sweet potato root tubers. Therefore, this study aimed to determine the optimum application rate of N in sweet potatoes to maximize yields and increase growth efficiency. Four levels of N (50, 100, 150, and 200 kg/ha) were applied as treatments. A randomized complete block design was used, and each treatment was replicated five times. Vine length, leaf length, stem thickness, and chlorophyll content were measured weekly, and the storage root yield was determined at the end of the experiment. The results showed a significant effect of the N treatments on plant growth, chlorophyll content, vine length, leaf area index and storage root yield. The chlorophyll content and vine length increased with an increase in the N rate. However, an inverse relationship was observed between storage root yield and N rates; the highest yield was recorded for the 50 kg/ha N treatment and the lowest yield was recorded for the 200 kg/ha N applied. Therefore, a rate of 50 to 100 kg/ha N is recommended for the production of orange-fleshed sweet potatoes. [ABSTRACT FROM AUTHOR]

Tibetan Plateau (TP) is known as the "Third Pole" of the Earth. Any changes in land surface processes on the TP can have an unneglectable impact on regional and global climate. With the warming and wetting climate, the land surface of the TP saw a darkening trend featured by decreasing surface albedo over the past decades, primarily due to the melting of glaciers, snow, and greening vegetation. Recent studies have investigated the effects of the TP land surface darkening on the field of climate, but these assessments only address one aspect of the feedback loop. How do these darkening-induced climate changes affect the frozen ground and ecosystems on the TP? In this study, we investigated the impact of TP land surface darkening on regional frozen ground and ecosystems using the state-of-the-art land surface model ORCHIDEE-MICT. Our model results show that darkening-induced climate changes on the TP will lead to a reduction in the area of regional frozen ground by 1.1×104±0.019×104 km2, a deepening of the regional permafrost active layer by 0.06±0.0004 m, and a decrease in the maximum freezing depth of regional seasonal frozen ground by 0.06±0.0016 m compared to the scenario without TP land surface darkening. Furthermore, the darkening-induced climate change on the TP will result in an increase in the regional leaf area index and an enhancement in the regional gross primary productivity, ultimately leading to an increase in regional terrestrial carbon stock by 0.81±0.001 PgC. This study addresses the remaining piece of the puzzle in the feedback loop of TP land surface darkening, and improves our understanding of interactions across multiple spheres on the TP. The exacerbated regional permafrost degradation and increasing regional terrestrial carbon stock induced by TP land surface darkening should be considered in the development of national ecological security barrier. [ABSTRACT FROM AUTHOR]

Organic farming is gaining recognition as a viable alternative to conventional methods, promising soil health preservation and sustained crop productivity with economic benefits. This study evaluated the physiological, growth, and yield responses of the PSB Rc18 rice variety and appraised its economic feasibility under different production systems. The experiment was laid out in Randomized Complete Block Design (RCBD) with four replications and three treatments: T1-best bet organic production system, T2-farmers' organic production system in Leyte, and T3-farmers' conventional production system in Leyte. The crop growth rate (CGR) of PSB Rc18 remained consistent across the different systems. However, the Net Assimilation Rate (NAR) peaked significantly between 42-56 days after transplanting (DAT) in the T2. Additionally, the Leaf Area Index (LAI) in T1 was comparable to that of T3. Rice grown under T1 reached heading and maturation earlier than T3. Although T3 produced the highest fresh straw, most productive tillers, and heaviest total biomass, the grain yield was similar across all production systems. Economically, T2 outperformed with a superior benefit-cost ratio of $0.55 and $0.94 per USD invested, considering both regular and premium prices for organic palay. These findings highlight organic farming practices' economic and agronomic viability, suggesting that promoting organic farming can be a beneficial alternative to conventional methods in Leyte. This study underscores the potential for integrating organic practices to enhance sustainability and economic outcomes in rice production, making both T1 and T2 significant options for farmers in Eastern Visayas. [ABSTRACT FROM AUTHOR]

SPAtial EFficiency (SPAEF) metric is one of the most thoroughly used metrics in hydrologic community. In this study, our aim is to improve SPAEF by replacing the histogram match component with other statistical indices, i.e. kurtosis and earth mover's distance, or by adding a fourth or fifth component such as kurtosis and skewness. The existing spatial metrics i.e. SPAtial efficiency (SPAEF), structural similarity (SSIM) and spatial pattern efficiency metric (SPEM) were compared with newly proposed metrics to assess their converging performance. The mesoscale hydrologic model (mHM) of the Moselle River is used to simulate streamflow (Q) and actual evapotranspiration (AET). The two-source energy balance AET during the growing season is used as monthly reference maps to calculate the spatial performance of the model. The moderate resolution imaging spectroradiometer based leaf area index is utilized by the mHM via pedo-transfer functions and multi-scale parameter regionalization approach to scale the potential ET. In addition to the real monthly AET maps, we also tested these metrics using a synthetic true AET map simulated with a known parameter set for a randomly selected day. The results demonstrate that the newly developed four-component metric i.e. SPAtial Hybrid 4 (SPAH4) slightly outperforms conventional three-component metric i.e. SPAEF (3% better). However, SPAH4 significantly outperforms the other existing metrics i.e. 40% better than SSIM and 50% better than SPEM. We believe that other fields such as remote sensing, change detection, function space optimization and image processing can also benefit from SPAH4. [ABSTRACT FROM AUTHOR]

Ridge–furrow cropping patterns, nitrogen fertilisation and seed rate regulation are popular management strategies for improving crop yields in the semi‐arid areas of Northwest China, but their interactive effects on grain yield and water use efficiency remain poorly understood. In 2020–2021 and 2021–2022, a two‐season field experiment was conducted on winter wheat. There were two cropping patterns (C), ridge–furrow cropping with film mulch (RC) and traditional cropping without mulch (TC), two nitrogen fertilisation rates (N), 0 and 200 kg N ha−1 (N0 and N1) and three seed rates (S), 240, 360 and 480 plants m−2 (S1, S2 and S3). The study was conducted in a split–split design with three replications (randomised blocks) and a total of 24 experimental plots. It was found that the interactive effects of C × N, C × S and N × S were significant on soil temperature (ST), leaf area index (LAI), relative chlorophyll content (SPAD), photosynthetic parameters, grain yield (GY) and water use efficiency (WUE) (p < 0.05), while C × N × S was significant only for LAI, aboveground biomass (AGB), GY and WUE (p < 0.05). Compared with TC and N0, RC and N1 significantly increased SPAD value (2.4% and 15.8%), net photosynthetic rate (Pn) (19.8% and 32.8%), net photosynthetic rate (Pn), transpiration rate (Tr) (7.0% and 15.7%) and the effective PSII quantum production (ΦPSII) (10.7% and 5.0%). The highest GY (6773 kg ha−1 over 2020–2021 and 8036 kg ha−1 over 2021–2022) and WUE (20.03 kg ha−1 mm−1 over 2020–2021, and 21.77 kg ha−1 mm−1 over 2021–2022) of winter wheat were observed under RC + N1 + S2. The findings showed that the RC cropping pattern with fertilisation and seed rate regulation (360 plants m−2) of winter wheat, which is appropriate for ensuring the long‐term sustainability of agricultural production in the semi‐arid regions of Northwest China, enhanced plant growth, photosynthetic traits, yield and water use efficiency. The study might give useful information for enhancing the productivity and water use efficiency of winter wheat in this and other similar climate locations. [ABSTRACT FROM AUTHOR]

Heat and drought stress, which often co-occur due to water evaporation, are two major abiotic factors limiting the production of rice (Oryza sativa L.) It is crucial to enhance understanding of the effects of these abiotic stresses in rice, particularly for rice-producing countries like Malaysia, which has yet to achieve rice selfsufficiency. This greenhouse study was conducted to evaluate the morphological changes of two important Malaysian cultivars (‘MR219’ and ‘MR303’) at vegetative, reproductive, and ripening stages, as well as their physiological response and yield components under normal (control), heat, drought, and combined heatdrought stress conditions. Individual heat stress greatly influenced rice growth and yield, with significant differences (p < 0.01) observed across all examined parameters except the grain to leaf area index ratio (GtoLAI). Conversely, individual drought stress mostly affected yield-related parameters, with significant differences (p < 0.01) in grain weight (GW), harvest index (HI), and percentage of filled grain (%FG). Interestingly, the combined stresses in this study did not significantly affect plant height (PH) for all growth stages and most yield-related traits (HI, GW, and GtoLAI). The majority of the significant changes (p < 0.01) were observed on physiological traits, including chlorophyll a (Chl A) and b (Chl B). We found a positive correlation between HI and %FG (R² = 0.3974**) under heat and drought stress, indicating that improving either of these traits can boost rice production. Collectively, our study revealed that the individual effects of heat and drought on rice growth and yield can differ from the effects of combined stress. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the evaporation (E) and transpiration (T) components of evapotranspiration (ETc), and the growth parameters of curly lettuce (Lactuca sativa L. cv. Caipira) grown under different irrigation treatments. The study was conducted in a Mediterranean-type plastic greenhouse located in Antalya, Türkiye, in the fall and spring growing seasons of 2020 and 2021, respectively. To assess the impact of water stress on ETc and its components, three different irrigation water levels [ I100 for full irrigation treatment (100%), I66 for 66% (I100×0.66), and I33 for 33% (I100×0.33)] were selected. Planted and unplanted pots were used to measure ETc and E independently. The values obtained from these measurements were used as inputs to calculate the evaporation that occurred in the soil under the crop canopy and plant transpiration. In the present study, T was determined indirectly from the difference of measured evapotranspiration and evaporation and estimated with the modified Hernandez-Suarez model (Te). The modified model for the different irrigation treatments showed high Te estimation performance. Evaporation from the soil in the planted pots (Es) was calculated by considering the canopy cover and soil water content. The study revealed that water stress significantly affected lettuce plant height, root length, cover percentage, leaf area index (LAI), number of leaves, fresh and dry head weights, and root weights (p<0.01). The study also investigated the relationship between Es/ETc , and LAI using an exponential method and established a strong nonlinear relationship in all irrigation treatments (R2>0.90). The modified model developed for different irrigation treatments in Mediterranean-type greenhouses can be used to predict lettuce ETc values with greater precision and to better understand the partitioning of ETc into its constituent components. [ABSTRACT FROM AUTHOR]

[Objective] The aim of this study is to examine the response of hydrological cycle factors to vegetation dynamics and climate change in the inland river basin of Inner Mongolia grassland, which is of important theoretical significance for the ecological protection and regional water resources development and utilization in the inland river basin of Inner Mongolia grassland. [Methods] The inland river basin of Inner Mongolia grassland was taken as the research area. Based on multi-source remote sensing, climate, meteorology, hydrology and other data, the temporal and spatial evolution and driving factors of hydrological cycle elements in the basin were analyzed by trend test, significance test and correlation analysis. [Results] (1) The evapotranspiration of the basin showed a significant upward trend (0.994 mm/a), the precipitation showed a significant downward trend (1.965 mm/a), and the soil moisture showed an increasing trend. (2) The vegetation in the growing season generally showed an increasing trend. The normalized difference vegetation index (NDVI), gross primary productivity (GPP) and leaf area index (LAI) showed a ladder-like spatial distribution pattern of high level in the east and low level in the west, and the temperature showed an overall upward trend. (3) Vegetation change was positively correlated with evapotranspiration (ET) and soil moisture (SSM). There was a significant positive correlation between temperature and evapotranspiration (R= 0.699, p=0.01), and the spatial correlation increased from west to east. There was a negative correlation between temperature and runoff, and a significant negative correlation with soil moisture in the east and west of the basin. [Conclusion] The hydrological cycle variables (precipitation, evapotranspiration, soil moisture, runoff) increased with the increase of vegetation. Precipitation, soil moisture and runoff decreased with the increase of temperature, and evapotranspiration increased with the increase of temperature. [ABSTRACT FROM AUTHOR]

Vegetation‐related processes, such as evapotranspiration (ET), irrigation water withdrawal, and groundwater recharge, are influencing surface water (SW)—groundwater (GW) interaction in irrigation districts. Meanwhile, conventional numerical models of SW‐GW interaction are not developed based on satellite‐based observations of vegetation indices. In this paper, we propose a novel methodology for multivariate assimilation of Sentinel‐based leaf area index (LAI) as well as in‐situ records of streamflow. Moreover, the GW model is initially calibrated based on water table observations. These observations are assimilated into the SWAT‐MODFLOW model to accurately analyze the advantage of considering high‐resolution LAI data for SW‐GW modeling. We develop a data assimilation (DA) framework for SWAT‐MODFLOW model using the particle filter based on the sampling importance resampling (PF‐SIR). Parameters of MODFLOW are calibrated using the parameter estimation (PEST) algorithm and based on in‐situ observation of the GW table. The methodology is implemented over the Mahabad Irrigation Plain, located in the Urmia Lake Basin in Iran. Some DA scenarios are closely examined, including univariate LAI assimilation (L‐DA), univariate streamflow assimilation (S‐DA), and multivariate streamflow‐LAI assimilation (SL‐DA). Results show that the SL‐DA scenario results in the best estimations of streamflow, LAI, and GW level, compared to other DA scenarios. The streamflow DA does not improve the accuracy of LAI estimation, while the LAI assimilation scenario results in significant improvements in streamflow simulation, where, compared to the open loop run, the (absolute) bias decreases from 75% to 6%. Moreover, S‐DA, compared to L‐DA, underestimates irrigation water use and demand as well as potential and actual crop yield. Key Points: Using source code modification, SWAT‐MODFLOW is connected to sequential DAMultivariate assimilation of streamflow, GW‐level and leaf area index (LAI) shows the best resultsStreamflow data assimilation does not improve LAI simulation, while LAI data assimilation improves streamflow simulation [ABSTRACT FROM AUTHOR]

This study was conducted to predict the grain yield of a maize crop from easy-to-measure growth parameters and select the best treatment combinations of biochar, inorganic fertilizer, and irrigation for the maize grain yield and water use efficiency (WUE) using the Principal Component Analysis (PCA) technique. Two rates of biochar (0 and 20 t ha−1) and fertilizer (0 and 300 kg ha−1) were applied to the soil, with maize crop planted, and subjected to deficit irrigation at 60, 80, and 100% of full irrigation amounts (FIA). Maize growth parameters (number of leaves—NL, leaf area—LA, leaf area index—LAI, and plant height—PH) were measured weekly. The results showed that the developed principal component regression (PCR) from the easy-to-measure growth parameters were strong and moderate in predicting the maize yield and WUE, with coefficient of determination; r2 values of 0.92 and 0.56, respectively. Using the PCA technique, the integration of irrigation with the least amount of water (60% FAI) with biochar (20 t ha−1) and fertilizer (300 kg ha−1) produced the highest ranking on grain yield and water use efficiency. This optimization technique showed that with the adoption of the integrative approach, 40% of irrigation water could be saved for other agricultural purposes [ABSTRACT FROM AUTHOR]

This study investigates the intricate interactions between strip spacings, water-fertilizer treatments, and the ensuing impact on the physiological attributes and productivity of maize and soybean intercropping systems. Implementing a 2:4 maize-soybean planting configuration within the Ningxia Yellow River irrigation zone, the research scrutinizes the effects of strip spacing (A), irrigation allocation (W), and fertilization intensity (F) on the physiological dynamics, net assimilation rate, leaf area index, and yield metrics of both crops and their integrated systems. It aims to provide comprehensive technical insights for the widespread application of strip intercropping strategies in maize and soybean agroecosystems. Findings reveal nuanced influences, notably the dominant effects of A during critical growth stages of maize (contributing rates of 75.6% to 83.7% during jointing and 52.4% to 81.3% during grain filling) and W primarily impacting the tasseling stage (contributing rates of 91.4% to 92.5%). Moreover, A significantly shapes soybean physiological indices during flowering (contributing rates of 97.9% to 99.3%), while W predominantly impacts pod setting (contributing rate of 70%). Treatment T6 (A2W3F1), characterized by heightened irrigation and reduced fertilizer application, augments maize net assimilation rate (NAR) and leaf area index (LAI) but exhibits an inverse relationship with soybean LAI. The tripartite factors significantly influence both system yield and economic benefits, ranking in order of W>A>F. Notably, all levels of A and W significantly affect maize yield and system productivity, particularly A2>A3>A1 and W3>W2> W1, while the F level shows negligible impact on soybean yield. Contribution analyses underscore A2>A3>A1, W3>W2>W1, and F1>F3> F2 in terms of system economic benefits. Specifically, A2 presents a 25.2% higher benefit compared to A1, W3 demonstrates a 32.3% higher benefit than W1, and F1 exhibits an 11.2% higher benefit over F2. [ABSTRACT FROM AUTHOR]

Land surface changes can have substantial impacts on biosphere–atmosphere interactions. In South America, rainforests abundantly emit biogenic volatile organic compounds (BVOCs), which, when coupled with pyrogenic emissions from deforestation fires, can have substantial impacts on regional air quality. We use novel and long-term satellite records of five trace gases, namely isoprene (C 5 H 8), formaldehyde (HCHO), methanol (CH 3 OH), carbon monoxide (CO), and nitrogen dioxide (NO 2), in addition to aerosol optical depth (AOD), vegetation (land cover and leaf area index), and burned area. We characterise the impacts of biogenic and pyrogenic emissions on atmospheric composition for the period 2001 to 2019 in the southern Amazon, a region of substantial deforestation. The seasonal cycle for all of the atmospheric constituents peaks in the dry season (August–October), and the year-to-year variability in CO, HCHO, NO 2 , and AOD is strongly linked to the burned area. We find a robust relationship between the broadleaf forest cover and total column C 5 H 8 (R2 = 0.59), while the burned area exhibits an approximate fifth root power law relationship with tropospheric column NO 2 (R2 = 0.32) in the dry season. Vegetation and burned area together show a relationship with HCHO (R2 = 0.23). Wet-season AOD and CO follow the forest cover distribution. The land surface variables are very weakly correlated with CH 3 OH, suggesting that other factors drive its spatial distribution. Overall, we provide a detailed observational quantification of biospheric process influences on southern Amazon regional atmospheric composition, which in future studies can be used to help constrain the underpinning processes in Earth system models. [ABSTRACT FROM AUTHOR]

Groundwater level (GWL) forecasting with machine learning has been widely studied due to its generally accurate results and low input data requirements. Furthermore, machine learning models for this purpose can be set up and trained quickly compared to the effort required for process-based numerical models. Despite demonstrating high performance at specific locations, applying the same model architecture to multiple sites across a regional area can lead to varying accuracies. The reasons behind this discrepancy in model performance have been scarcely examined in previous studies. Here, we explore the relationship between model performance and the geospatial and time series features of the sites. Using precipitation (P) and temperature (T) as predictors, we model monthly groundwater levels at approximately 500 observation wells in Lower Saxony, Germany, applying a 1-D convolutional neural network (CNN) with a fixed architecture and hyperparameters tuned for each time series individually. The GWL observations range from 21 to 71 years, resulting in variable test and training dataset time ranges. The performances are evaluated against selected geospatial characteristics (e.g. land cover, distance to waterworks, and leaf area index) and time series features (e.g. autocorrelation, flat spots, and number of peaks) using Pearson correlation coefficients. Results indicate that model performance is negatively influenced at sites near waterworks and densely vegetated areas. Longer subsequences of GWL measurements above or below the mean negatively impact the model accuracy. Besides, GWL time series containing more irregular patterns and with a higher number of peaks might lead to higher model performances, possibly due to a closer link with precipitation dynamics. As deep learning models are known to be black-box models missing the understanding of physical processes, our work provides new insights into how geospatial and time series features link to the input–output relationship of a GWL forecasting model. [ABSTRACT FROM AUTHOR]

The cultivation of sunflower (Helianthus annuus L.) as a cut flower stands out in floriculture due to its aesthetic beauty and commercial value. Understanding how cut sunflower genotypes adapt to different edaphoclimatic regions and management practices is essential to optimize flower quality and productivity. This study aimed to evaluate the effect of plant density and location on the development, growth, and quality of cut sunflower in tropical and subtropical environments. Plant densities of 10, 20, 30, 40, and 50 plants/m2 were evaluated in tropical climate and subtropical climate using a randomized block design in a factorial scheme. Results showed significant differences between locations for plant height, capitulum and stem diameter, final number of leaves, leaf area, leaf area index, phyllochron, and the developmental cycle. Plant density significantly influenced these variables except for plant height and developmental cycle. The interaction between location and plant density was significant only for capitulum diameter and final leaf number. The findings indicate that both planting density and location significantly influence the developmental cycle of cut sunflowers, with lower densities favoring more robust plants at harvest. A density of 30 plants/m2 is recommended for efficient space use without significantly compromising floral stem quality. All produced stems are marketable, suggesting that adjusting planting density can optimize production without compromising quality, adapting to specific regional conditions. [ABSTRACT FROM AUTHOR]

As a vital indicator for measuring the coupled carbon–water cycle of an ecosystem, water use efficiency (WUE) can also reflect the adaptive capacity of plants in different ecosystems. Located in Southwest China, the Li River Basin has a representative karst landform, and the uneven rainfall in the region leads to severe water shortage. In this study, we analyzed the spatial–temporal transformation characteristics of the WUE of the basin and its relationship with different influencing factors from 2001 to 2020 based on a correlation analysis and trend analysis. The main conclusions are as follows: (1) The average value of WUE in the Li River Basin was 1.8251 gC· mm−1·m−2, and it kept decreasing at a rate of 0.0072 gC· mm−1·m−2·a−1 in the past 20 years. With respect to the spatial distribution of the multi-year average of WUE, it exhibits a gradual increasing trend from west to east. (2) Between gross primary productivity (GPP) and evapotranspiration (ET), it was found that ET was the primary influencing factor of WUE. Precipitation was positively correlated with WUE in the Li River Basin, accounting for 67.22% of the total area of the basin. The air temperature was negatively correlated with WUE, and the area was negatively correlated with WUE, accounting for 92.67% of the basin area. (3) The normalized difference vegetation index (NDVI) and leaf area index (LAI) were negatively correlated with WUE, and the proportions of negatively correlated areas to the total area of the basin were similar; both were between 60 and 70%. The growth of vegetation inhibited the increase in WUE in the basin to a certain extent. Regarding Vapor Pressure Deficit (VPD), the proportions of positive and negative correlation areas with WUE were similar, accounting for 49.58% and 50.42%, respectively. (4) The occurrence of drought events and the enhancement in its degree led to a continuous increase in WUE in the basin; for different land cover types, the correlation of the standardized precipitation evapotranspiration index (SPEI) was in the following order from strongest to weakest: grassland > cropland > forest > shrubland. [ABSTRACT FROM AUTHOR]

BACKGROUND: In order to address the issues of uneven pesticide deposition and low pesticide utilization in rubber gardens caused by the traditional diffuse plant protection spraying method, this study focuses on the air‐assisted powder sprayer and proposes a variable pesticide application control system. A variable pesticide application decision‐making model integrating the leaf area index (LAI) was designed based on powdery mildew control standards and individual rubber tree information. According to the target powder spraying accuracy requirements, a control model of the air velocity adjustment device was established and a fuzzy proportional‐integral‐differential (PID) air velocity control system was developed. RESULTS: The simulation results indicate that the wind speed control system exhibits a maximum overshoot of 2.18% and an average response time of 1.48 s. The field experiment conducted in a rubber plantation revealed that when the air‐assisted powder sprayer operates in the variable powder spraying mode, the average response time of the control system is 2.5 s. The control accuracy of each executive mechanism exceeded 95.9%. The deposition coefficient of variation (CV) at different canopy heights was relatively consistent, with values of 35.38%, 36.26% and 36.90%. In comparison to the quantitative mode, the variable mode showed a significant 20.03% increase in the effective utilization rate of sulfur powder. CONCLUSION: These research findings provide valuable technical support for the advancement of mechanized variable powder spraying equipment in rubber tree cultivation. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Dependent upon the relatively severe ecological conditions of the forest stands at the highland landscapes, they sometimes experience transformations. Forest stand tree species, which are a bit more tolerant to these conditions, gain advantage over the others, and hence often tend to intrude into the nearby stands, transforming them and influencing their canopy characteristics. At a highland landscape of Western Black Sea Region in Turkey, a sessile oak stand, which had remained young and pure along the 20 years (1986–2006), has then been invaded by the Bornmüllerian fir seeds from the surrounding stands and consequently been transformed into young-mature mixed stand throughout the subsequent 15 years (2006–2021). Therefore, then intruded Bornmüllerian firs have primarily and gradually altered the physiological characteristics of the canopies within this mixed stand. The aim of this study is to monitor and analyse the inter-annual physiological changes of these tree canopies using some canopy parameters following the occurrence of the species mixture. The stand canopy physiological characteristics were monitored and analysed by hemispherical photographs and associated parameters obtained from them. These canopy parameters; Leaf Area Index (LAI), Light Transmission (LT), Gap Fraction (GF), Canopy Openness (CO), were acquired for the Junes of the years, 2015, 2018 and 2021. The mean LAI had increased almost 0.50 m2 m–2 within the stand along the eight years period (2007–2015). However, it could only increase 0.20 m2 m–2 along the subsequent six years monitoring period. On the other hand, the percentage values of the other canopy parameters had accordingly decreased. This situation indicated that the first occurrence of the Bornmüllerian fir canopies together with the canopies of the sessile oaks along those eight years period had led to the sudden rise of the mean LAI. Nevertheless, the gradual and slight increment of the mean LAI along the subsequent six years monitoring period, has been attributed to the normal physiological development of the tree species; Bornmülerian firs in particular. In fact, due to the case with the study parcel, the highland landscape has potentially been experiencing gradual increment in the percentage of eco-physiologically more tolerant Bornmüllerian firs and conversely gradual decline in the percentage of eco-physiologically sensitive sessile oaks, whose sustainability within highland landscape should be supported with the ecological and comprehensive management proposals. [ABSTRACT FROM AUTHOR]

Biogenic volatile organic compounds (BVOCs) are regarded as important precursors for ozone and secondary organic aerosol, mainly from vegetation emissions. In the context of the expanding trend of vegetation greening, the development of high‐precision vegetation data and accurate BVOC emission estimates are essential to develop effective air pollution control measures. In this study, by integrating the multi‐source vegetation cover data, we established a high‐resolution vegetation distribution (HRVD) data set to develop a high spatio‐temporal resolution emission inventory and investigated the impact of different land cover data sets on emission simulation and impact of land cover change on BVOC emissions during 2001–2020. The annual total BVOC emissions in China for 2020 was 15.66 Tg, which were mainly from trees. The emissions simulated by CNLUCC and MODIS data sets were 1.53% and 1.72% higher than those simulated by HRVD data sets, respectively. The spatial distribution of emission differences was consistent with that of land cover differences. The simulated BVOC emissions by the HRVD data set had the best accuracy as they improved the bias between modeling and observation from 69.06% to 65.35% and decreased the underprediction of observations by a factor of 2.13 compared with simulation by MEGAN default vegetation data. The annual BVOC emissions caused by changing vegetation distribution and LAIv (LAI of vegetation covered surfaces) enhanced at a rate of 72.06 Gg yr−1 during 2001–2020. LAIv was the main driver of emission variations. The total OH reactivity of the resulted BVOC emissions increased at a rate of 1.59 s−1 yr−1, with isoprene contributed the most. Plain Language Summary: Biogenic Volatile organic compounds (BVOCs) are the key precursors of fine particulate matter and ozone, that mainly from vegetation emissions. To help to develop effective air pollution control measures in the context of expanding vegetation coverage for realizing carbon neutralization in China, it is urgent to develop highly precise vegetation data and accurately estimate BVOC emission. A high‐resolution vegetation distribution data set was established through integrating multi‐source vegetation cover data. Using it, the simulated annual BVOC emission in China was 15.66 Tg and mainly emitted from trees. Emissions from varied growth forms had different compound compositions. The BVOC emission simulated using the high‐resolution vegetation distribution data set we developed had better accuracy than that using the single vegetation databases. The annual BVOC emissions caused by changing vegetation cover and leaf area index (LAI) enhanced at a rate of 72.06 Gg yr−1 during 2001–2020. LAI was the main driver of BVOC emission variations. The interannual variation and its spatial pattern of the OH loss rates of BVOCs during 2001–2020 were consistent with that of BVOC emissions, especially isoprene. Key Points: Annual total BVOC emissions in China for 2020 was 15.66 Tg and emissions from varied growth forms had different compositionsBVOC emission inventory simulated by the developed high‐resolution vegetation distribution (HRVD) data set had better accuracyBVOC emission enhanced at a rate of 72.06 Gg yr−1 during 2001–2020 caused by land cover change, mainly driven by changing leaf area index [ABSTRACT FROM AUTHOR]

Background: Understanding the complex relationship between vegetation dynamics and land surface temperature (LST) is crucial for comprehending ecosystem functioning, climate change impacts, and sustainable land management. Hence, this study conducts a temporal analysis of leaf area index (LAI) and LST data derived from Sentinel-2 and Landsat Operational Land Imagery (OLI) in the Mille River Basin, a tropical region in Ethiopia. LAI data were generated using Sentinel-2 imagery processed with the Sentinel Application Platform (SNAP) toolbox, an open-access earth observation analysis tool, while Landsat OLI collection 2 level 2 data were utilized for precise LST retrieval. The Mann–Kendall test was used to detect trends in the time series data. Results: The trends in the mean LAI were statistically significant at P values of 0.05 and 0.10 for the annual and seasonal trends, respectively. The mean LST trends were insignificant throughout the study period except for the summer season, for which the P value was 0.07. The correlation between the LAI and LST was weak (R2 = 0.36) during the crop-growing seasons (summer and spring) but moderate in winter (R2 = 0.46) and autumn (R2 = 0.41). Conclusion: The findings of this research clarify the complex relationships between variations in surface temperature and vegetation growth patterns, providing insight into the environmental mechanisms driving the dynamics of localized ecosystems. The study underscores the implications of these findings for informed decision-making in sustainable land management, biodiversity conservation, and climate change mitigation strategies. [ABSTRACT FROM AUTHOR]

AbstractAgro-physiology, C-assimilation & its contribution in sink development; and crop yield can be augmented through N regimes for higher productivity of scented rice (aromatic and basmati). Being scented rice sink limited crop; we loomed to enhance sink capacity by augmenting leaf area index (LAI) though nitrogen (N) regimes in new environment. In order to establish N-relationship with sink capacity (SC) & filling efficiency (SFE) and to determine N-related agro-physiological behavior a field experiment for two years conducted in middle IGP. Four N levels (control, low, moderate and high) tested for two aromatic and two basmati rice varieties under split plot design. The basmati rice varieties reported 37.81-52.13% higher grain yield (GY) with lesser N over short grained aromatic varieties. Dry matter, its translocation and pre-anthesis contribution, tiller number and physiological traits at growth stages were also comparatively higher in HUBR 10–9 over short grained HUR 917 and comprehended parity level with HUR 4–3. The SFE was negatively correlated with SC and LAI across nitrogen dose and varieties. Higher LAI at moderate N produced larger sink; higher SFE was at low N. On contrary to short grained HUR 917; BS varieties (HUBR 10–9 & HUBR 2–1) produced larger sink. Economic N was lower for BSV’s (126.48–131.52 kg N ha−1) with GY potential of 5.11–6.01 Mg ha−1. [ABSTRACT FROM AUTHOR]

Water scarcity limits the amount of maize that can be produced in Ethiopia and around the world. This study was aimed to investigate the effects of amendment application and arbuscular mycorrhizal fungi (AMF) inoculation on maize growth and biomass yield in a greenhouse under both normal and water‐stressed conditions. Biochar, organic matter, and blended inorganic fertilizer (NPSB) were added as amendments. Inoculation of Gigaspora rosea and Rhizophagus clarus with the application of NPSB under normal watering has resulted in maximum heights of 96.7 and 115.0 cm, respectively. When they were subjected to water stress, their maximum heights were 65.7 and 68.01 cm. The leaf number in NPSB fertilization under regular watering was 13.3, comparable to 13.7 in R. clarus + OM and R. clarus + biochar + OM + NPSB. Under normal watering, G. rosea + NPSB and R. clarus + NPSB demonstrated the highest aboveground biomass yields of 57.7 and 47.8 g, respectively. In comparison to the control, G. rosea and R. clarus + NPSB demonstrated the highest yields of 52.9 and 42.8 g. The treatments involving G. rosea + biochar + OM + NPSB and R. clarus + biochar + OM + NPSB exhibited a greater leaf area index of 31.42 and 26.8, respectively. The treatments that were inoculated with G. rosea also showed improvements in root colonization and spore density. The study's findings showed that both a single and a combination AMF inoculation improved every growth parameter that was looked at. To find out how well native AMF can boost maize plant yield in both normal and water‐stressed environments, more research is necessary. [ABSTRACT FROM AUTHOR]

China's Losses Plateau (LP) is one of the ecologically vulnerable and the most severe soil erosion regions. Thus, knowing spatiotemporal changes in evapotranspiration (ET) and its components (soil evaporation, E; transpiration, T; and vegetation interception evaporation, EI) and revealing the underlying mechanisms are vital for ecosystem and water resources sustainability for this region. Here, we investigate the spatiotemporal changes in ET and its components and then quantify the impacts of climate variables (i.e., precipitation, radiation, temperature, and relative humidity) and vegetation dynamics (e.g., land use/cover changes [LUCC] and changes in leaf area index [LAI]) on their annual trends, by using a process‐based terrestrial ecosystem model and a joint‐solution method with multiple sensitivity numerical experiments. Results show that over 67% of the study region experienced significant (p < 0.05) increases in annual ET, T, and EI, with regional average rises of 4.05, 3.67, and 0.74 mm·year−1, respectively. However, there are significant (p < 0.05) decreases in regional mean E of 0.38 mm·year−1, and the negative trend covers 35.8% of the study area. E, T, and EI changes dominate the annual ET trends over 11.8%, 87.3%, and 0.9% of the study area, respectively. Attribution analyses highlight the increased LAI as the critical factor governing these trends across most of the LP (>58%). At the same time, precipitation and LUCC play a more dominant role in the remaining areas. This study emphasizes the spatial heterogeneity in the drivers of changes in ET and its components and highlights the critical role of vegetation dynamics. These findings provide valuable insights for understanding the ET processes and guiding sustainable water resource management in the LP. [ABSTRACT FROM AUTHOR]

The aim of the present research was to evaluate the efficiency of different vegetation indices (VI) obtained from satellites with varying spatial resolutions in discriminating the phenological stages of soybean crops. The experiment was carried out in a soybean cultivation area irrigated by central pivot, in Balsas, MA, Brazil, where weekly assessments of phenology and leaf area index were carried out. Throughout the crop cycle, spectral data from the study area were collected from sensors, onboard the Sentinel-2 and Amazônia-1 satellites. The images obtained were processed to obtain the VI based on NIR (NDVI, NDWI and SAVI) and RGB (VARI, IV GREEN and GLI), for the different phenological stages of the crop. The efficiency in identifying phenological stages by VI was determined through discriminant analysis and the Algorithm Neural Network–ANN, where the best classifications presented an Apparent Error Rate (APER) equal to zero. The APER for the discriminant analysis varied between 53.4% and 70.4% while, for the ANN, it was between 47.4% and 73.9%, making it not possible to identify which of the two analysis techniques is more appropriate. The study results demonstrated that the difference in sensors spatial resolution is not a determining factor in the correct identification of soybean phenological stages. Although no VI, obtained from the Amazônia-1 and Sentinel-2 sensor systems, was 100% effective in identifying all phenological stages, specific indices can be used to identify some key phenological stages of soybean crops, such as: flowering (R1 and R2); pod development (R4); grain development (R5.1); and plant physiological maturity (R8). Therefore, VI obtained from orbital sensors are effective in identifying soybean phenological stages quickly and cheaply. [ABSTRACT FROM AUTHOR]

Introduction: Monitoring the leaf area index (LAI), which is directly related to the growth status of rice, helps to optimize and meet the crop's fertilizer requirements for achieving high quality, high yield, and environmental sustainability. The remote sensing technology of the unmanned aerial vehicle (UAV) has great potential in precisionmonitoring applications in agriculture due to its efficient, nondestructive, and rapid characteristics. The spectral information currently widely used is susceptible to the influence of factors such as soil background and canopy structure, leading to low accuracy in estimating the LAI in rice. Methods: In this paper, the RGB andmultispectral images of the critical periodwere acquired through rice field experiments. Based on the remote sensing images above, the spectral indices and texture information of the rice canopy were extracted. Furthermore, the texture information of various images at multiple scales was acquired through resampling, which was utilized to assess the estimation capacity of LAI. Results and discussion: The results showed that the spectral indices (SI) based on RGB and multispectral imagery saturated in the middle and late stages of rice, leading to low accuracy in estimating LAI.Moreover, multiscale texture analysis revealed that the texture of multispectral images derived from the 680 nm band is less affected by resolution, whereas the texture of RGB images is resolution dependent. The fusion of spectral and texture features using random forest and multiple stepwise regression algorithms revealed that the highest accuracy in estimating LAI can be achieved based on SI and texture features (0.48 m) from multispectral imagery. This approach yielded excellent prediction results for both high and low LAI values. With the gradual improvement of satellite image resolution, the results of this study are expected to enable accurate monitoring of rice LAI on a large scale. [ABSTRACT FROM AUTHOR]

Irrigated cultivation exerts a significant influence on the local climate and the hydrological cycle. The North China Plain (NCP) is known for its intricate agricultural system, marked by expansive cropland, high productivity, compact rotation, a semi-arid climate, and intensive irrigation practices. As a result, there has been considerable attention on the potential impact of this intensive irrigated agriculture on the local climate. However, studying the irrigation impact in this region has been challenging due to the lack of an accurate simulation of crop phenology and irrigation practices within the climate model. By incorporating double cropping with interactive irrigation, our study extends the capabilities of the Weather Research Forecast (WRF) model, which has previously demonstrated commendable performance in simulating single-cropping scenarios. This allows for two-way feedback between irrigated crops and climate, further enabling the inclusion of irrigation feedback from both ground and vegetation perspectives. The improved crop modeling system shows significant enhancement in capturing vegetation and irrigation patterns, which is evidenced by its ability to identify crop stages, estimate field biomass, predict crop yield, and project monthly leaf area index. The improved simulation of large-scale irrigated crops in the NCP can further enhance our understanding of the intricate relationship between agricultural development and climate change. [ABSTRACT FROM AUTHOR]