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Following soil disturbances, establishing healthy roadside vegetation can reduce surface water runoff, improve soil quality, decrease erosion, and enhance landscape aesthetics. This study explores the use of organic soil amendments (OAs) as alternatives to conventional vegetation growth approaches, aiming to provide optimal compost mixing ratios for poor soils, and clarify guidelines for OAs' use in roadside projects. Three sandy loam soils and one loam soil were chosen for the study. Organic amendments included yard waste (Y), food waste (F), turkey litter and green waste-based (T) composts, and wood-derived biochar (B). Treatment applications targeted specific increases in the organic matter (OM) percentage of the soils. A selection of seven native species (grasses and forbs) in a total of 156 pots (4 control soils + 4 soils x 4 OAs x 3 application rates, all prepared in triplicates) was used for the pot study experiment. A significant correlation between electrical conductivity (soluble salts) in soil-OA blends and corresponding percent green coverage (%GC) was found. High salts from the T compost either delayed or curtailed growth. Notably, 3 out of the 4 soils amended with biochar exhibited rapid vegetation coverage during initial growth stages compared to other soil-OA blends but reduced the nitrogen (N) uptake and leaf area in black-eyed Susan (BES) plants. In contrast, N uptake was higher in the BES plants emerging from composts T, F, and Y compared to biochar. It is recommended to minimize concentrated manure-based (e.g., turkey litter) composts for roadside projects as an OM source, and alternatively, enriching wood-based biochar with nutrients when used as a soil amendment. Within the current study, composts such as F and Y were well-suited to establish healthy and long-lasting vegetation.

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The plant-available soil phosphorus rate and methods for applying phosphatic fertilizer and soil P-fixation capacity are critical factors for lower cotton productivity in Southern Punjab, Pakistan. Hence, a two-year study was conducted in Central Cotton Research Institute (CCRI), Multan, Pakistan, to examine the effects of various P rates and application methods on cotton crop output during the growing seasons of 2014 and 2015. Phosphorus was applied in four rates (0, 40, 80, and 120 kg ha-1 P2O5) using broadcast, band application, and fertigation methods. Results indicated that the impact of P rates was statistically significant on plant height, the number of nodes, monopodial and sympodial branches, leaf area index, harvest index, and seed cotton yield. The greater P application (120 kg P2O5 ha-1) had a better effect on cotton productivity than the lower application rates (0, 40, and 80 kg P2O5 ha-1). The band application responded better on nodes plant-1, sympodial branches plant-1, boll weight, leaf area index, lint yield, and harvest during the growing season 2015. Therefore, by adopting the band application coupled with 120 kg P2O5 ha-1 rather than the conventional method of broadcast, productivity of cotton crops could be increased.

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Leaf area is one of the important parameters for plant canopy development. It is used as an indicator closely related to plant growth in several studies on plant production. However, most leaf area meters used today are costly and rely on human observations. This situation may be limiting for researchers in terms of having proper leaf area measuring devices. The reliance on human-focused measurements leads to human errors. Digital scanners and cameras, digital image processing-based estimation methods, paper weighing, grid counting, regression equations, width and height correlation models, planimeters, laser optics, and handheld scanners can be used to determine leaf area. However, some of these methods are expensive and unnecessary for simple studies. Therefore, this study aims to design and implement an embedded system with a simpler, cheaper alternative to the currently used methods and devices, minimizing human errors. The proposed embedded system serves as a tool for measuring leaf area using a photovoltaic panel (PV) and an Adaptive Neuro-Fuzzy Inference System (ANFIS). In the study, geometric shapes with known areas are used as the learning data, and real plant leaves with known areas are used in the testing process. As a result, the prediction made by ANFIS is observed to have an accuracy of R 2  = 0.99.

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BACKGROUND: Leaf area index (LAI) is an important indicator for assessing plant growth and development, and is also closely related to photosynthesis in plants. The realization of rapid accurate estimation of crop LAI plays an important role in guiding farmland production. In study, the UAV-RGB technology was used to estimate LAI based on 65 winter wheat varieties at different fertility periods, the wheat varieties including farm varieties, main cultivars, new lines, core germplasm and foreign varieties. Color indices (CIs) and texture features were extracted from RGB images to determine their quantitative link to LAI. RESULTS: The results revealed that among the extracted image features, LAI exhibited a significant positive correlation with CIs (r = 0.801), whereas there was a significant negative correlation with texture features (r = -0.783). Furthermore, the visible atmospheric resistance index, the green-red vegetation index, the modified green-red vegetation index in the CIs, and the mean in the texture features demonstrated a strong correlation with the LAI with r > 0.8. With reference to the model input variables, the backpropagation neural network (BPNN) model of LAI based on the CIs and texture features (R2 = 0.730, RMSE = 0.691, RPD = 1.927) outperformed other models constructed by individual variables. CONCLUSION: This study offers a theoretical basis and technical reference for precise monitor on winter wheat LAI based on consumer-level UAVs. The BPNN model, incorporating CIs and texture features, proved to be superior in estimating LAI, and offered a reliable method for monitoring the growth of winter wheat. © 2024 Society of Chemical Industry.

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Rising atmospheric carbon dioxide concentrations ([CO2]) affect crop growth and the associated hydrological cycle through physiological forcing, which is mainly regulated by reducing stomatal conductance (gs) and increasing leaf area index (LAI). However, reduced gs and increased LAI can affect crop water consumption, and the overall effects need to be quantified under elevated [CO2]. Here we develop a SWAT-gs-LAI model by incorporating a nonlinear gs-CO2 equation and a missing LAI-CO2 relationship to investigate the responses of water consumption of grain maize, maize yield, and losses of water and soil to elevated [CO2] in the Upper Mississippi River Basin (UMRB; 492,000 km2). Results exhibited enhanced maize yield with decreased water consumption for increases in [CO2] from 495 ppm to 825 ppm during the historical period (1985-2014). Elevated [CO2] promoted surface runoff but suppressed sediment loss as the predominant impact of LAI-CO2 leading to enhanced surface cover. A comprehensive analysis of future climate change showed increased maize water consumption in comparison to the historical period, driven by the more pronounced effects of overall climate change rather than solely elevated [CO2]. Generally, future climate change promoted maize yield in most regions of the UMRB for three Shared Socioeconomic Pathway (SSP) scenarios. Surface runoff was shown to increase generally in the future with sediment loss increasing by an average of 0.39, 0.42, and 0.66 ton ha-1 for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. This was due to negative climatic change effects largely surpassing the positive effect of elevated [CO2], particularly in zones near the middle and lower stream. Our results underscore the crucial role of employing a physically-based model to represent crop physiological processes under elevated [CO2] conditions, improving the reliability of predictions related to crop growth and the hydrological cycle.

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Climate change has become a concern, emphasizing the need for the development of crops tolerant to drought. Therefore, this study is designed to explore the physiological characteristics of quinoa that enable it to thrive under drought and other extreme stress conditions by investigating the combined effects of irrigation water levels (100%, 75%, and 50% of quinoa's water requirements, WR as I1, I2 and I3) and different planting methods (basin, on-ridge, and in-furrow as P1, P2 and P3) on quinoa's physiological traits and gas exchange. Results showed that quinoa's yield is lowest with on-ridge planting and highest in the in-furrow planting method. Notably, the seed protein concentrations in I2 and I3 did not significantly differ but they were 25% higher than those obtained in I1, which highlighted the possibility of using a more effective irrigation method without compromising the seed quality. On the other hand, protein yield (PY) was lowest in P2 (mean of I1 and I2 as 257 kg ha-1) and highest in P3 (mean of I1 and I2 as 394 kg ha-1, 53% higher). Interestingly, PY values were not significantly different in I1 and I2, but they were lower significantly in I3 by 28%, 27% and 20% in P1, P2, and P3, respectively. Essential plant characteristics including plant height, stem diameter, and panicle number were 6.1-16.7%, 6.4-24.5%, and 18.4-36.5% lower, respectively, in I2 and I3 than those in I1. The highest Leaf Area Index (LAI) value (5.34) was recorded in the in-furrow planting and I1, while the lowest value was observed in the on-ridge planting method and I3 (3.47). In I3, leaf temperature increased by an average of 2.5-3 oC, particularly during the anthesis stage. The results also showed that at a similar leaf water potential (LWP) higher yield and dry matter were obtained in the in-furrow planting compared to those obtained in the basin and on-ridge planting methods. The highest stomatal conductance (gs) value was observed within the in-furrow planting method and full irrigation (I1P3), while the lowest values were obtained in the on-ridge and 50%WR (I3P2). Finally, photosynthesis rate (An) reduction with diminishing LWP was mild, providing insights into quinoa's adaptability to drought. In conclusion, considering the thorough evaluation of all the measured parameters, the study suggests using the in-furrow planting method with a 75%WR as the best approach for growing quinoa in arid and semi-arid regions to enhance production and resource efficiency.

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The leaf area index (LAI) is a crucial physiological indicator of crop growth. This paper introduces a new spectral index to overcome angle effects in estimating the LAI of crops. This study quantitatively analyzes the relationship between LAI and multi-angle hyperspectral reflectance from the canopy of winter oilseed rape (Brassica napus L.) at various growth stages, nitrogen application levels and coverage methods. The angular stability of 16 traditional vegetation indices (VIs) for monitoring the LAI was tested under nine view zenith angles (VZAs). These multi-angle VIs were input into machine learning models including support vector machine (SVM), eXtreme gradient boosting (XGBoost), and Random Forest (RF) to determine the optimal monitoring strategy. The results indicated that the back-scattering direction outperformed the vertical and forward-scattering direction in terms of monitoring the LAI. In the solar principal plane (SPP), EVI-1 and REP showed angle stability and high accuracy in monitoring the LAI. Nevertheless, this relationship was influenced by experimental conditions and growth stages. Compared with traditional VIs, the observation perspective insensitivity vegetation index (OPIVI) had the highest correlation with the LAI (r = 0.77-0.85). The linear regression model based on single-angle OPIVI was most accurate at -15° (R2 = 0.71). The LAI monitoring achieved using a multi-angle OPIVI-RF model had the higher accuracy, with an R2 of 0.77 and with a root mean square error (RMSE) of 0.38 cm2·cm-2. This study provides valuable insights for selecting VIs that overcome the angle effect in future drone and satellite applications.

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African pastoralists suffer recurrent droughts that cause high livestock mortality and vulnerability to climate change. The index-based livestock insurance (IBLI) program offers protection against drought impacts. However, the current IBLI design relying on the normalized difference vegetation index (NDVI) may pose limitation because it does not consider the mixed composition of rangelands (including herbaceous and woody plants) and the diverse feeding habits of grazers and browsers. To enhance IBLI, we assessed the efficacy of utilizing distinct browse and grazing forage estimates from woody LAI (LAIW) and herbaceous LAI (LAIH), respectively, derived from aggregate leaf area index (LAIA), as an alternative to NDVI for refined IBLI design. Using historical livestock mortality data from northern Kenya as reference ground dataset, our analysis compared two competing models for (1) aggregate forage estimates including sub-models for NDVI, LAI (LAIA); and (2) partitioned biomass model (LAIP) comprising LAIH and LAIW. By integrating forage estimates with ancillary environmental variables, we found that LAIP, with separate forage estimates, outperformed the aggregate models. For total livestock mortality, LAIP yielded the lowest RMSE (5.9 TLUs) and higher R2 (0.83), surpassing NDVI and LAIA models RMSE (9.3 TLUs) and R2 (0.6). A similar pattern was observed for species-specific livestock mortality. The influence of environmental variables across the models varied, depending on level of mortality aggregation or separation. Overall, forage availability was consistently the most influential variable, with species-specific models showing the different forage preferences in various animal types. These results suggest that deriving distinct browse and grazing forage estimates from LAIP has the potential to reduce basis risk by enhancing IBLI index accuracy.

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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.

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Background: Deciduous forests in eastern North America experienced a widespread and intense spongy moth (Lymantria dispar) infestation in 2021. This study quantified the impact of this spongy moth infestation on carbon (C) cycle in forests across the Great Lakes region in Canada, utilizing high-resolution (10 × 10 m2) Sentinel-2 satellite remote sensing images and eddy covariance (EC) flux data. Study results showed a significant reduction in leaf area index (LAI) and gross primary productivity (GPP) values in deciduous and mixed forests in the region in 2021. Results: Remote sensing derived, growing season mean LAI values of deciduous (mixed) forests were 3.66 (3.18), 2.74 (2.64), and 3.53 (2.94) m2 m-2 in 2020, 2021 and 2022, respectively, indicating about 24 (14)% reduction in LAI, as compared to pre- and post-infestation years. Similarly, growing season GPP values in deciduous (mixed) forests were 1338 (1208), 868 (932), and 1367 (1175) g C m-2, respectively in 2020, 2021 and 2022, showing about 35 (22)% reduction in GPP in 2021 as compared to pre- and post-infestation years. This infestation induced reduction in GPP of deciduous and mixed forests, when upscaled to whole study area (178,000 km2), resulted in 21.1 (21.4) Mt of C loss as compared to 2020 (2022), respectively. It shows the large scale of C losses caused by this infestation in Canadian Great Lakes region. Conclusions: The methods developed in this study offer valuable tools to assess and quantify natural disturbance impacts on the regional C balance of forest ecosystems by integrating field observations, high-resolution remote sensing data and models. Study results will also help in developing sustainable forest management practices to achieve net-zero C emission goals through nature-based climate change solutions.

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Aboveground biomass (AGB) is an important indicator of the grassland ecosystem. It can be used to evaluate the grassland productivity and carbon stock. Satellite remote sensing technology is useful for monitoring the dynamic changes in AGB across a wide range of grasslands. However, due to the scale mismatch between satellite observations and ground surveys, significant uncertainties and biases exist in mapping grassland AGB from satellite data. This is also a common problem in low- and medium-resolution satellite remote sensing modeling that has not been effectively solved. The rapid development of uncrewed aerial vehicle (UAV) technology offers a way to solve this problem. In this study, we developed a method with UAV and satellite synergies for estimating grassland AGB that filled the gap between satellite observation and ground surveys and successfully mapped the grassland AGB in the Hulunbuir meadow steppe in the northeast of Inner Mongolia, China. First, based on the UAV hyperspectral data and ground survey data, the UAV-based AGB was estimated using a combination of typical vegetation indices (VIs) and the leaf area index (LAI), a structural parameter. Then, the UAV-based AGB was aggregated as a satellite-scale sample set and used to model satellite-based AGB estimation. At the same time, spatial information was incorporated into the LAI inversion process to minimize the scale bias between UAV and satellite data. Finally, the grassland AGB of the entire experimental area was mapped and analyzed. The results show the following: (1) random forest (RF) had the best performance compared with simple regression (SR), partial least squares regression (PLSR) and back-propagation neural network (BPNN) for UAV-based AGB estimation, with an R2 of 0.80 and an RMSE of 76.03 g/m2. (2) Grassland AGB estimation through introducing LAI achieved higher accuracy. For UAV-based AGB estimation, the R2 was improved by an average of 10% and the RMSE was reduced by an average of 9%. For satellite-based AGB estimation, the R2 was increased from 0.70 to 0.75 and the RMSE was decreased from 78.24 g/m2 to 72.36 g/m2. (3) Based on sample aggregated UAV-based AGB and an LAI map, the accuracy of satellite-based AGB estimation was significantly improved. The R2 was increased from 0.57 to 0.75, and the RMSE was decreased from 99.38 g/m2 to 72.36 g/m2. This suggests that UAVs can bridge the gap between satellite observations and field measurements by providing a sufficient training dataset for model development and AGB estimation from satellite data.

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The quantification of green space green plot ratio (GPR) is mostly based on estimation formulas, and the leaf area index (LAI) estimation values in these estimation formulas have not been well verified by measured LAI values, resulting in errors and uncertainties in GPR quantification results. This study aims to address this gap by measuring the LAI of 113 regional plants in Chongqing, China, following a standardized measurement path for digital hemispherical photography (DHP). The results indicate that the optimal relative exposure value (REV) was - 1 under overcast conditions and - 2 under sunny and cloudy conditions. Among the threshold algorithms for hemispherical images, the Intermodes algorithm in ImageJ was the best. The LAI of regional plants is highest in summer, followed by spring and autumn, and lowest in winter. Tree height (h) and crown width (w) are key factors affecting LAI, but the LAI also varies with plant species. Overall, the LAI of evergreen trees is higher than that of deciduous trees. The LAI of evergreen trees and shrubs with a height shorter than 5 m is the largest, and that of deciduous trees and shrubs with a crown width larger than 8 m is the largest. The study further verified that the existing GPR estimation formula exhibited large errors in Chongqing, while there was a strong correlation (R2 = 0.973) between the GPR estimation value and the measured value. A conversion formula was developed to reduce estimation biases, and the corrected formula is capable of estimating GPR values more accurately when actual LAI measurements are insufficient. Overall, this study verifies the significance of measuring localized LAI values, promotes the understanding of LAI suitability for GPR calculations, and provides an empirical formula for GPR estimation in Chongqing, China.

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Precise and timely leaf area index (LAI) estimation for winter wheat is crucial for precision agriculture. The emergence of high-resolution unmanned aerial vehicle (UAV) data and machine learning techniques offers a revolutionary approach for fine-scale estimation of wheat LAI at the low cost. While machine learning has proven valuable for LAI estimation, there are still model limitations and variations that impede accurate and efficient LAI inversion. This study explores the potential of classical machine learning models and deep learning model for estimating winter wheat LAI using multispectral images acquired by drones. Initially, the texture features and vegetation indices served as inputs for the partial least squares regression (PLSR) model and random forest (RF) model. Then, the ground-measured LAI data were combined to invert winter wheat LAI. In contrast, this study also employed a convolutional neural network (CNN) model that solely utilizes the cropped original image for LAI estimation. The results show that vegetation indices outperform the texture features in terms of correlation analysis with LAI and estimation accuracy. However, the highest accuracy is achieved by combining both vegetation indices and texture features to invert LAI in both conventional machine learning methods. Among the three models, the CNN approach yielded the highest LAI estimation accuracy (R 2 = 0.83), followed by the RF model (R 2 = 0.82), with the PLSR model exhibited the lowest accuracy (R 2 = 0.78). The spatial distribution and values of the estimated results for the RF and CNN models are similar, whereas the PLSR model differs significantly from the first two models. This study achieves rapid and accurate winter wheat LAI estimation using classical machine learning and deep learning methods. The findings can serve as a reference for real-time wheat growth monitoring and field management practices.

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Canopy structure and understory light have important effects on forest productivity and the growth and distribution of the understory. However, the effects of stand composition and season on canopy structure and understory light environment (ULE) in the subtropical mountain Pinus massoniana forest system are poorly understood. In this study, the natural secondary P. massoniana-Castanopsis eyrei mixed forest (MF) and P. massoniana plantation forest (PF) were investigated. The study utilized Gap Light Analyzer 2.0 software to process photographs, extracting two key canopy parameters, canopy openness (CO) and leaf area index (LAI). Additionally, data on the transmitted direct (Tdir), diffuse (Tdif), and total (Ttot) radiation in the light environment were obtained. Seasonal variations in canopy structure, the ULE, and spatial heterogeneity were analyzed in the two P. massoniana forest stands. The results showed highly significant (P < 0.01) differences in canopy structure and ULE indices among different P. massoniana forest types and seasons. CO and ULE indices (Tdir, Tdif, and Ttot) were significantly lower in the MF than in the PF, while LAI was notably higher in the MF than in the PF. CO was lower in summer than in winter, and both LAI and ULE indices were markedly higher in summer than in winter. In addition, canopy structure and ULE indices varied significantly among different types of P. massoniana stands. The LAI heterogeneity was lower in the MF than in the PF, and Tdir heterogeneity was higher in summer than in winter. Meanwhile, canopy structure and ULE indices were predominantly influenced by structural factors, with spatial correlations at the 10 m scale. Our results revealed that forest type and season were important factors affecting canopy structure, ULE characteristics, and heterogeneity of P. massoniana forests in subtropical mountains.

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Premise: Leaf epidermal cell morphology is closely tied to the evolutionary history of plants and their growth environments and is therefore of interest to many plant biologists. However, cell measurement can be time consuming and restrictive with current methods. CuticleTrace is a suite of Fiji and R-based functions that streamlines and automates the segmentation and measurement of epidermal pavement cells across a wide range of cell morphologies and image qualities. Methods and Results: We evaluated CuticleTrace-generated measurements against those from alternate automated methods and expert and undergraduate hand tracings across a taxonomically diverse 50-image data set of variable image qualities. We observed ~93% statistical agreement between CuticleTrace and expert hand-traced measurements, outperforming alternate methods. Conclusions: CuticleTrace is a broadly applicable, modular, and customizable tool that integrates data visualization and cell shape measurement with image segmentation, lowering the barrier to high-throughput studies of epidermal morphology by vastly decreasing the labor investment required to generate high-quality cell shape data sets.

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Machine learning (ML) techniques offer a promising avenue for improving the integration of remote sensing data into mathematical crop models, thereby enhancing crop growth prediction accuracy. A critical variable for this integration is the leaf area index (LAI), which can be accurately assessed using proximal or remote sensing data based on plant canopies. This study aimed to (1) develop a machine learning-based method for estimating the LAI in rice and soybean crops using proximal sensing data and (2) evaluate the performance of a Remote Sensing-Integrated Crop Model (RSCM) when integrated with the ML algorithms. To achieve these objectives, we analyzed rice and soybean datasets to identify the most effective ML algorithms for modeling the relationship between LAI and vegetation indices derived from canopy reflectance measurements. Our analyses employed a variety of ML regression models, including ridge, lasso, support vector machine, random forest, and extra trees. Among these, the extra trees regression model demonstrated the best performance, achieving test scores of 0.86 and 0.89 for rice and soybean crops, respectively. This model closely replicated observed LAI values under different nitrogen treatments, achieving Nash-Sutcliffe efficiencies of 0.93 for rice and 0.97 for soybean. Our findings show that incorporating ML techniques into RSCM effectively captures seasonal LAI variations across diverse field management practices, offering significant potential for improving crop growth and productivity monitoring.

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Understanding the future trends of carbon and water fluxes between terrestrial ecosystems and the atmosphere is crucial for predicting Earth's climate dynamics. This study employs an advanced numerical approach to project global gross primary productivity (GPP) and evapotranspiration (ET) from 2001 to 2100 under various climate scenarios based on Shared Socioeconomic Pathways (SSPs). To improve predictions of vegetation dynamics, we introduce a novel model (CoLM-PVPM), an enhancement of the Common Land Model version 2014 (CoLM2014), incorporating a prognostic vegetation phenology model (PVPM). Compared to CoLM2014 that relies on satellite-based leaf area index (LAI) inputs, CoLM-PVPM predicts LAI time series using climate variables. Model validation using historical data from 2001 to 2010 demonstrates PVPM in capturing spatiotemporal variations in satellite LAI. Our modeling results indicate that annual averaged LAI and total GPP increase under SSP1-2.6 but decrease under SSP2-4.5, SSP3-7.0, and SSP5-8.5 by 2100. By comparison, annual total ET consistently increases under all SSP scenarios by 2100. Global annual averaged LAI is highly correlated with annual total GPP in all scenarios, while its correlation with annual total ET weakens in SSP2-4.5, SSP3-7.0, and SSP5-8.5. Global annual total vapor pressure deficit (VPD) and precipitation are highly correlated with annual total ET in all scenarios. As emission levels increase, the negative correlation between annual total VPD and GPP strengthens, while the correlation between annual total precipitation and GPP weakens. This research presents an improved model for predicting terrestrial vegetation processes and underscores the importance of low carbon emission scenarios in maintaining carbon-water balances in specific regions.

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There is a growing demand for technologies able to decrease the environmental impact of agricultural activities without penalizing quali-quantitative characteristics of productions. In the case of viticulture, one of the key problems is represented by the spray drift during fungicide treatments. The diffusion in operational farming contexts of technologies based on variable-rate and recycling tunnel sprayers is often limited by their cost and, for the latter, by their size and lower maneuverability, representing clear disadvantages especially in case of small farms or in hilly and mountain areas. We present a new digital technology implemented in a mobile app that supports the reduction of both the number of treatments and the amount of fungicide distributed per treatment. The technology is based (i) on an alert system that prevents unneeded treatments in case of no risk of infection and (ii) on the quantification of the optimal amounts of active ingredients and dilution water based on the sprayer type/settings and on leaf area index values estimated with a common smartphone. An internal database allows to adjust (in case of need) the active ingredient dose to assure full compliance with product's legal requirements. In case of heterogeneity in leaf area index values inside the vineyard, prescription maps are generated. Results from a 2-year case study in a vineyard in northern Italy are shown, where the system allowed to reduce by 26.4 % and 27.4 % (mean of two years), respectively, the seasonal amounts of fungicides and dilution water, and by 43.8 % the copper content in must. The high usability of the technology proposed (just a common smartphone is needed) and the fact that it does not require updating the farm machine park highlights the suitability of the proposed solution for operational farming conditions, including premium wine production districts often characterized by small farms in hilly areas.

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Gross primary production (GPP) is a critical component of the global carbon cycle and plays a significant role in the terrestrial carbon budget. The impact of environmental factors on GPP can occur through both direct (by influencing photosynthetic efficiency) and indirect (through the modulation of vegetation structure) pathways, but the extent to which these mechanisms contribute has been seldom quantified. In this study, we used structural equation modeling and observations from the FLUXNET network to investigate the direct and indirect effects of environmental factors on terrestrial ecosystem GPP at multiple temporal scales. We found that canopy structure, represented by leaf area index (LAI), is a crucial intermediate factor in the GPP response to environmental drivers. Environmental factors affect GPP indirectly by altering canopy structure, and the relative proportion of indirect effects decreased with increasing LAI. The study also identified different effects of environmental factors on GPP across time scales. At the half-hourly time scale, radiation was the primary driver of GPP. In contrast, the influences of temperature and vapor pressure deficit took on greater prominence at longer time scales. About half of the total effect of temperature on GPP was indirect through the regulation of canopy structure, and the indirect effect increased with increasing time scale (GPPNT-based models: 0.135 (half-hourly) vs. 0.171 (daily) vs. 0.189 (weekly) vs. 0.217 (monthly); GPPDT-based models: 0.139 vs. 0.170 vs. 0.187 vs. 0.215; all values were reported in gC m-2 d-1 °C-1, P < 0.001); while the indirect effect of radiation on GPP was comparatively lower, accounting for less than a quarter of the total effect. Furthermore, we observed a direct, negative-to-positive impact of precipitation on GPP across timescales. These findings provide crucial information on the interplay between environmental factors and LAI on GPP and enable a deeper understanding of the driving mechanisms of GPP.

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