Nectarines and peaches are valuable crops that require significant investment when establishing a new orchard. Orchard rejuvenation through grafting mature trees, known as top-grafting or top-working, provides a timelier and therefore more economical establishment of a fruit bearing orchard than whole orchard renewal. Limited research is available on stone fruit tree grafting on nursery seedlings and literature on top-worked fruit trees is lacking. There is also no information on how methods of irrigation would affect the growth and fruit yield of top-grafted nectarine trees. In this study, research was carried out in a mature peach orchard by top-working the peach trees with nectarine scions under three different irrigation methods: furrow, drip, and micro-spray. Tree growth and fruit production were evaluated from initial grafting to maturity over six growing seasons. The furrow- irrigated nectarine scions grew about 5

Crop production in the desert Southwest of the United States, as well as in other arid and semi-arid regions, requires tools that provide accurate crop evapotranspiration (ET) estimates to support efficient irrigation management. Such tools include the web-based OpenET platform, which provides real-time ET data generated from six satellite-based models, their Ensemble, and a field-based system (LI-710, LI-COR Inc., Lincoln, NE, USA). This study evaluated simulated ET (ETSIM) of cotton (Gossypium hirsutum L.) derived from OpenET models (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and SSEBop), their Ensemble approach, and LI-710. Field data were utilized to estimate cotton ET using the soil water balance (SWB) method (ETSWB) from June to October 2025 in Gila Bend, AZ, USA. Four evaluation metrics, the normalized root-mean-squared error (NRMSE), mean bias error (MBE), simulation error (Se), and coefficient of determination (R2), were employed to evaluate the performance of OpenET models, their Ensemble, and the LI-710 in estimating cotton ET. Statistical analysis indicated that the ALEXI/DisALEXI, geeSEBAL, and PT-JPL models substantially underestimated ETSWB, with simulation errors ranging from -26.92% to -20.57%. The eeMETRIC, SIMS, SSEBop, and Ensemble provided acceptable ET estimates (22.57% <= NRMSE <= 29.85%, -0.36 mm. day-1 <= MBE <= 0.16 mm. day-1, -7.58% <= Se <= 3.42%, 0.57 <= R2 <= 0.74). Meanwhile, LI-710 simulated cotton ET acceptably with a slight tendency to overestimate daily ET by 0.21 mm. A strong positive correlation was observed between daily ETSIM from LI-710 and ETSWB, with Se and NRMSE of 4.40% and 23.68%, respectively. Based on our findings, using a singular OpenET model, such as eeMETRIC, SIMS, or SSEBop, the OpenET Ensemble, and the LI-710 can offer growers and decision-makers reliable guidance for efficient irrigation management of late-planted cotton in arid and semi-arid climates.

Environmental observation networks, such as AmeriFlux, are foundational for monitoring ecosystem response to climate change, management practices, and natural disturbances; however, their effectiveness depends on their representativeness for the regions or continents. We proposed an empirical, time series approach to quantify the similarity of ecosystem fluxes across AmeriFlux sites. We extracted the diel and seasonal characteristics (i.e., amplitudes, phases) from carbon dioxide, water vapor, energy, and momentum fluxes, which reflect the effects of climate, plant phenology, and ecophysiology on the observations, and explored the potential aggregations of AmeriFlux sites through hierarchical clustering. While net radiation and temperature showed latitudinal clustering as expected, flux variables revealed a more uneven clustering with many small (number of sites < 5), unique groups and a few large (> 100) to intermediate (15-70) groups, highlighting the significant ecological regulations of ecosystem fluxes. Many identified unique groups were from under-sampled ecoregions and biome types of the International Geosphere-Biosphere Programme (IGBP), with distinct flux dynamics compared to the rest of the network. At the finer spatial scale, local topography, disturbance, management, edaphic, and hydrological regimes further enlarge the difference in flux dynamics within the groups. Nonetheless, our clustering approach is a data-driven method to interpret the AmeriFlux network, informing future cross-site syntheses, upscaling, and model-data benchmarking research. Finally, we highlighted the unique and underrepresented sites in the AmeriFlux network, which were found mainly in Hawaii and Latin America, mountains, and at under-sampled IGBP types (e.g., urban, open water), motivating the incorporation of new/unregistered sites from these groups.

Selenium (Se) biofortification is a plant-based strategy to increase Se content in crops. Consumption of Se-enriched crops can enhance Se intake for populations experiencing mild/moderate Se deficiencies. In these two different Se biofortification studies, Se accumulation was evaluated in field-grown tomatoes (Solanum lycopersicon). In year 1, organic Se as Se-enriched plant material (Stanleya pinnata; 350 mg Se kg(-1) DW) was directly applied to soil at three different rates (100, 200, and 400 g Se ha-(1)). In year 2, organic Se extracted from S. pinnata was applied to soil at two different rates (50 and 100 g Se ha(-1)). On the field site, the light-textured soil was previously amended with biochar (softwood feedstock) 3-years earlier to enhance water retention in soil during drought conditions in California. For both experiments, irrigation water was applied at two different rates (50 and 100 % ETo). In year 1, Se concentrations were significantly (p < 0.05) increased to 0.39 mu g Se g(-1) DW in fruit grown with application of S. pinnata to soil and in year 2, fruit Se concentrations significantly (p < 0.05) increased to 0.07 mu g Se g-1 DW with high rates of extractable Se from S. pinnata applied to soil, irrespective of biochar or irrigation treatment for both years. For both experiments, Se speciation of the tomato fruit showed that selenomethionine was the major selenoamino acid significantly detected, followed by SeCys2, selenite, and selenate with all treatments. In conclusion, these two Se biofortification studies have demonstrated that organic Se applied as either Se-enriched plant material or as extractable organic Se, can result in Se biofortified fruit. Although crops like tomatoes grown in such soils are sensitive to drought and insufficient irrigation (<100 % ETo), the addition of softwood biochar did not result in significant changes in fruit Se accumulation in either biofortification experiment.

Abstract The ecosystem benefits linked to intercropping and diversified agroecosystems is an area with increasing research interest, particularly in sustainable food production and farm resilience to extreme climate variability. Interrow cropping of alfalfa (Medicago sativa L.) in almond [Prunus dulcis (Mill.) D. A. Webb] orchards during the 3–4 non‐bearing, establishment years has potential to advance sustainable intensification in agricultural regions such as the Central Valley of California. In this study we evaluated ecosystem benefits linked to this intercropped agroecosystem in contrast to conventional almond systems with interrow spaces maintained bare. From Winter 2023 to Spring 2024 (157 days), we modeled soil hydrological properties (HYDRUS‐1D) and quantified soil nitrogen using various approaches. Simulation from HYDRUS revealed that winter soil evaporative loss was most substantial for a flood‐irrigated bare‐soil control (208.1 mm) and lowest for the alfalfa intercropped interrow (59.2 mm). Estimated soil water storage was lowest in the alfalfa intercropped interrow and highest for bare‐soil controls, indicating continuous plant water uptake throughout the winter period when almond trees are dormant. Winter soil N loss measured using suction lysimeters, ion exchange soil resins traps, and soil sampling (0–120 cm) indicated that N leaching was greatest in the bare‐soil interrow spaces and lowest for alfalfa intercropped treatment. The utilization of free winter inputs, such as rainwater and slow‐release mineralized N from dairy manure compost, translated to a 2.22 tonne ha−1 alfalfa yield and equated to a $500 ha−1 gross revenue for the first alfalfa cutting. Overall, the preliminary ecosystem benefits observed in this unique alfalfa–almond intercropped agroecosystem were attributed to augmentation in farm resource use efficiency and revenues generated during the winter season.

Selenium (Se) biofortification is a plant-based strategy to increase Se composition in crops by adding Se onto the crop or to the soil. Consumption of Se-enriched crops may increase Se intake needed for people living in Sedeficient regions. In this 3-year biofortification study, Se accumulation and growth were evaluated in onion (Allium cepa L.) as affected by the applied Se form (selenate, selenite, selenomethionine), Se application technique (soil vs foliar), addition of biochar originating from softwood feedstock, and irrigation rate (50, 75, 100 % ETo). Results showed Se concentrations were highest in onions treated with foliar application of selenate, irrespective of biochar added to soil or water application rate. Selenium speciation of the onion showed that gammaglutamyl-Se-selenomethyl-selenocysteine (gamma-glu-MeSeCys) was detected at the greatest level in the onion bulb, compared to methylselenocysteine (MeSeCys) and selenomethionine (SeMet), irrespective of treatments. The combination of foliar Se application, biochar, and deficit irrigation at 50 % ETo, reduced onion bulb fresh weight compared to biochar addition under high irrigation treatments. In conclusion, foliar application of selenate at 75-100 % ETo, with or without biochar, was most effective for increasing Se and gamma-glu-MeSeCys concentrations in onion bulbs grown in a light textured soil.

BackgroundThe development of alfalfa cultivars with improved digestibility may minimize the yield-quality tradeoff, enabling higher quality with late-harvested forage and possibly higher yields. MethodsAn irrigated experiment conducted over 4 years compared 28-d harvest schedules with 35-d harvest schedules and an alternating 21-d and 35-d schedule. Four conventional cultivars and four cultivars developed for higher digestibility were grown under each schedule. ResultsDelayed cutting (35-d) yields were 16% greater and the staggered treatments were 6% higher than the 28-d strategy. The nutritive value decreased significantly with the 35-d schedule, but a "staggered" system provided nutritive value similar to the 28-d schedule while achieving higher yields. The nutritive value of cultivars was in the order of HarvXtra>Hi-Gest> conventional cultivars. The HarvXtra but not Hi-Gest cultivars achieved similar digestibility under the 35-d cutting schedule compared with conventional cultivars on a 28-d schedule. ConclusionsThis study clearly demonstrates that higher nutritive value cultivars of fall dormancy 6-9 grown with staggered or late cutting schedules can increase yields while maintaining higher nutritive value. The combination of staggered or late schedules with improved cultivars can maximize yields while maintaining the nutritive value of alfalfa, potentially breaking the alfalfa yield-quality tradeoff.

Moderate-Resolution Imaging Spectroradiometer (MODIS) Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) products are being increasingly used for the quantitative remote sensing of vegetation. However, the assumption underlying the MODIS NBAR product’s inversion model—that surface anisotropy remains unchanged over the 16-day retrieval period—may be unreliable, especially since the canopy structure of vegetation undergoes stark changes at the start of season (SOS) and the end of season (EOS). Therefore, to investigate the MODIS NBAR product’s temporal effect on the quantitative remote sensing of crops at different stages of the growing seasons, this study selected typical phenological parameters, namely SOS, EOS, and the intervening stable growth of season (SGOS). The PROBA-V bioGEOphysical product Version 3 (GEOV3) Fractional Vegetation Cover (FVC) served as verification data, and the Pearson correlation coefficient (PCC) was used to compare and analyze the retrieval accuracy of FVC derived from the MODIS NBAR product and MODIS Surface Reflectance product. The Anisotropic Flat Index (AFX) was further employed to explore the influence of vegetation type and mixed pixel distribution characteristics on the BRDF shape under different stages of the growing seasons and different FVC; that was then combined with an NDVI spatial distribution map to assess the feasibility of using the reflectance of other characteristic directions besides NBAR for FVC correction. The results revealed the following: (1) Generally, at the SOSs and EOSs, the differences in PCCs before vs. after the NBAR correction mainly ranged from 0 to 0.1. This implies that the accuracy of FVC derived from MODIS NBAR is lower than that derived from MODIS Surface Reflectance. Conversely, during the SGOSs, the differences in PCCs before vs. after the NBAR correction ranged between –0.2 and 0, suggesting the accuracy of FVC derived from MODIS NBAR surpasses that derived from MODIS Surface Reflectance. (2) As vegetation phenology shifts, the ensuing differences in NDVI patterning and AFX can offer auxiliary information for enhanced vegetation classification and interpretation of mixed pixel distribution characteristics, which, when combined with NDVI at characteristic directional reflectance, could enable the accurate retrieval of FVC. Our results provide data support for the BRDF correction timescale effect of various stages of the growing seasons, highlighting the potential importance of considering how they differentially influence the temporal effect of NBAR corrections prior to monitoring vegetation when using the MODIS NBAR product.

Remote sensing-based surface energy balance algorithms have been used to estimate water use of various crops. However, citrus evapotranspiration (ET) estimation is challenging mainly due to evergreen leaves and a clumped canopy structure. In this study, we evaluated the performance of two methods for calculating ET: the ensemble of OpenET models, which are mostly satellite thermal-based models, and the BAITSSS water and energy balance model. Calculated ET was compared with (i.) eddy covariance (EC) ET measurements and (ii.) water received (irrigation plus precipitation) data for two citrus orchards in San Joaquin Valley, California. Polaris-based soil hydraulic properties and measured volumetric water content were used for BAITSSS parameterization and initialization, respectively. Sentinel-2 based NDVI was used for BAITSSS simulation. Results showed that annual ET based on the OpenET ensemble model (1169 mm) was on average 30 % larger (r(2) similar to 0.71, RMSE similar to 1.16 mm) than both EC ET (908 mm) and water received (886 mm). The disparity mostly occurred in spring. BAITSSS, on the other hand, showed mixed results compared to observations (r(2) similar to 0.77, RMSE similar to 0.94 mm). Both measured from EC and modeled ET from BAITSSS and ensemble OpenET values were below grass reference ET (ETo) for the majority of the simulation period. Soil moisture and water received data indicated the orchards may have been deficit irrigated. Overall, this study highlights the challenges of ET modeling in citrus orchards and the need for improved estimation of ET for this specialty crop.

Alfalfa is one of the major perennial forage crops in California, vital for the livestock industry, and provides environmental benefits to the ecosystem in the state. Information on subsurface drip irrigation (SDI) practices on alfalfa is limited particularly in areas related to drip tape spacings and installation depths and their impacts on topsoil profile wetting patterns and alfalfa productivity. The objective of this study was to compare three different depths of drip lines: 15, 30, and 45 cm on topsoil profile wetting patterns and identify the optimum depth for achieving sustainable management practices for alfalfa production under SDI. Crop water requirements were determined using Tule Technologies system. Volumetric soil water contents from twelve cuts were simulated using HYDRUS-2D. Initial volumetric soil water contents from Watermark sensors were used in the model and measured volumetric soil water distributions were used for hydraulic conductivity calibration. Simulated results showed that there was no significant difference between root water uptake (RWU) among the various drip depths. RWU was 171.6, 170.0, and 168.2 cm at drip line depths of 15, 30, and 45 cm, respectively. Applied irrigation water during the study period was 195.1 cm while rainfall was 4.1 cm. A 10% reduction in topsoil volumetric soil water content was observed for drip lines at 30 cm depth as compared with 15 cm depth, while a 20% reduction in topsoil volumetric soil water content was observed for drip lines at 45 cm depth as compared with 15 cm depth. Drip lines at 30 cm depth are likely the optimal for RWU and free drainage; however, drip lines at 45 cm depth may allow growers to provide additional irrigation events (without increasing the topsoil volumetric soil water content) closer to the harvest date, potentially resulting in higher yield and water use efficiency.

Accurate crop status forecasting benefits from assimilating remote sensing observations and crop model simulations.When conducting data assimilation (DA) using an Ensemble Kalman filter (EnKF), arbitrary inflation factors are normally adopted to account for unspecified uncertainties, thus avoiding filter divergence.Here, we developed a Bayesian methodology in which the uncertainties were systematically quantified by combining disparate methods in one framework.Its applicability and performance with crop model GECROS using the EnKF framework were tested against data collected from two years of field experiments, in which aboveground biomass (W above ), grain weight (W grain ), aboveground nitrogen (N) content (N above ), grain N content (N grain ) and leaf traits like leaf dry weight, leaf N content and leaf area index were measured for rice.Using only the observations from the first year, the uncertain parameters in GECROS were calibrated by a Markov Chain Monte Carlo approach, while the parameters in the assumed error model that describes the uncertainties of crop model simulations were estimated simultaneously.The calibrated model parameters performed well in the validation year, except for the simulated leaf traits (Normalized Root Mean Squared Error (NRMSE ) > 0.38).Remotely sensed leaf traits predicted by a Gaussian Process Regression (GPR) model were more accurate (NRMSE < 0.34), with uncertainties of the remote sensing observations estimated from the GPR model itself.Assimilating simulated and predicted leaf traits with their estimated uncertainties into EnKF prevented filter divergence, and the forecast accuracy of crop model improved in the validation year.Compared with simulation without assimilating in-season remote sensing observations, the NRMSE of updated whole-season W above andN above both decreased from 0.37 to 0.20; and those of updated W grain andN grain at harvest decreased from 0.40 and 0.28 to 0.22 and 0.19, respectively.The developed method contributes to systematic uncertainty analysis in DA and accurate forecasting of in-season and end-of-season crop carbon and N status for smart farming.

Highlights UAV-based remote sensing platform and ML methods for individual tree level yield prediction. Decision tree ML model had an accuracy of 85% for yield prediction. Vegetation indices derived from UAV high-resolution imagery were used for yield prediction. Scale-aware prediction model has great potential for efficient field management. Abstract. Accurately estimating the yield is one of the most important research projects for orchard management. Growers need to estimate the yield of trees at an early stage to make smart decisions for field management. However, methods to predict the yield at the individual tree level are currently not available because of the complexity and variability of each tree. To improve the accuracy of yield prediction, the authors evaluate the performance of an unmanned aerial vehicle (UAV)-based remote sensing system and machine learning (ML) approaches for tree-level pomegranate yield estimation. A lightweight multispectral camera was mounted on the UAV platform to acquire high-resolution images. Eight features were extracted from the UAV images, including the normalized difference vegetation index (NDVI), the green normalized vegetation index (GNDVI), the red-edge normalized difference vegetation index (NDVIre), red-edge triangulated vegetation index (RTVIcore), individual tree canopy size, the modified triangular vegetation index (MTVI2), the chlorophyll index-green (CIg), and the chlorophyll index-rededge (CIre). Correlation coefficients (R2) were calculated between these vegetation indices and tree yield. Classic machine learning approaches were applied with the extracted features to predict the yield at the individual tree level. Results showed that the decision tree classifier had the best prediction performance, with an accuracy of 85%. The study demonstrated the potential of using UAV-based remote sensing methods, coupled with ML algorithms, for pomegranate yield estimation. Predicting the yield at the individual tree level will enable the stakeholders to manage the orchard at different scales, thus improving field management efficiency. Keywords: Machine learning, Remote sensing, UAV, Vegetation index, Yield prediction.

Agricultural Managed Aquifer Recharge (Ag-MAR) is a potential and sustainable practice where agricultural fields can be used to recharge depleted aquifers using excess precipitation during winter. However, there is little information on the amount of Ag-MAR that can be applied to crops such as alfalfa. HYDRUS-2D was used to estimate the net recharge in an alfalfa field grown on a sandy loam soil in a Mediterranean climate at Parlier, California, USA in 2020-2022. The alfalfa field had four irrigation treatments: full irrigation during summer growing season (March through November), mid-summer deficit irrigation treatment (March to August and complete irrigation cutoff after August cutting), winter flooding treatment, and no winter flooding. Recharge, evapotranspiration (ETa), soil moisture dynamics, and root water uptake were simulated during the recharge period in winter. Previously fully irrigated treatments in summer, followed by winter recharge led to cumulative groundwater recharge of 1459, 1687, and 1415 mm for 2020, 2021, and 2022, respectively. These applications resulted in a net recharge of 85%, 89%, and 84% of the applied irrigation water during the winter period, a significant contribution to groundwater aquifers. Mid-summer deficit irrigation treatments, followed by winter recharge, resulted in net groundwater recharge of 1337, 1498, and 1272 mm for 2020, 2021, and 2022, respectively, amounting to 78%, 79%, and 76% of the applied irrigation water during winter flooding periods. HYDRUS simulation model predicted groundwater recharge potential in these experiments successfully with a coefficient of determination, R2 values of 0.91, and 0.89 for the groundwater recharge during winter flooding after the full irrigation in summer, and the mid-summer deficit irrigation, respectively. These results confirm the potential utilization of HYDRUS simulations in predicting groundwater recharge potential under similar sandy -soil conditions in California's San Joaquin Valley.

Altered precipitation patterns and increased water demands from urban, industrial, and environmental needs often reduce the volume of irrigation water available for agriculture. Strategies that reduce irrigation water inputs, e.g., deficit irrigation (DI), need further evaluation to determine potential impacts on yield and soil microbial communities driving critical soil biogeochemical cycles. Whether soil organic amendments can stabilize DI effects on yield and microbial communities remains unknown. Processing tomato beds were established in a full factorial experimental design of soils unamended or amended with a one-time application—four to five years before sampling—of either biochar or biochar with compost and three irrigation regimes: full (100 % of plant water demand), or DI treatments at 75 % or 50 % of full irrigation. We profiled soil bacterial and archaeal community compositions for two growing seasons and determined soil C and N metabolic potentials and biomass of microbial groups using high throughput sequencing of 16S rRNA genes and phospholipid fatty acid analysis. We tested the discrete and interactive effects of DI and soil amendments on soil chemical and biological properties, and crop yield. DI had stronger effects on the bacterial and archaeal community composition than soil organic amendments. However, 75 % DI did not strongly affect bacterial and archaeal community composition or the total and individual biomass of microbial groups, but it increased irrigation water productivity of processing tomatoes. Although soil amendments did not stabilize the compositional shifts induced by DI, their recalcitrant C still had residual effects on the bacterial and archaeal community composition years after their incorporation. Furthermore, soil moisture correlated with bacterial and archaeal community's C and N metabolic potentials, likely augmenting the amendments' C and N residence time in DI soils. Our results provide insight into water-saving mechanisms that could increase profit margins in water-scarce years without affecting microbial populations that support plant growth and productivity.

Accurate simulation of plant water use across agricultural ecosystems is essential for various applications, including precision agriculture, quantifying groundwater recharge, and optimizing irrigation rates. Previous approaches to integrating plant water use data into hydrologic models have relied on evapotranspiration (ET) observations. Recently, the flux variance similarity approach has been developed to partition ET to transpiration (T) and evaporation, providing an opportunity to use T data to parameterize models. To explore the value of T/ET data in improving hydrologic model performance, we examined multiple approaches to incorporate these observations for vegetation parameterization. We used ET observations from 5 eddy covariance towers located in the San Joaquin Valley, California, to parameterize orchard crops in an integrated land surface – groundwater model. We find that a simple approach of selecting the best parameter sets based on ET and T performance metrics works best at these study sites. Selecting parameters based on performance relative to observed ET creates an uncertainty of 27% relative to the observed value. When parameters are selected using both T and ET data, this uncertainty drops to 24%. Similarly, the uncertainty in potential groundwater recharge drops from 63% to 58% when parameters are selected with ET or T and ET data, respectively. Additionally, using crop type parameters results in similar levels of simulated ET as using site-specific parameters. Different irrigation schemes create high amounts of uncertainty and highlight the need for accurate estimates of irrigation when performing water budget studies.

Organic soil amendments can improve soil health but their role in improving N management has not been well quantified. The objectives of this research were to evaluate the influence of biochar and manure compost application on crop yield, N uptake, changes in soil and environmental losses and to use the information to assess the N requirement and project fertilization needs. A field experiment was conducted in California, USA, with processing tomato ( Lycopersicon esculentum Mill.) grown first, followed by garlic ( Allium sativum ). The soil was a sandy loam with a pH of 7.2 and CEC of 9.1 cmol(+) kg −1 . Treatments included two biochar products, derived from almond shell or softwood feedstocks, applied at 20 or 40 tonnes (t) ha −1 , dairy manure compost at 20 t ha −1 , combinations of the manure and the biochar (each at 20 t ha −1 ), and a control. Although biochar and manure applications improved the surface soil organic C and total N content, no significant effects on crop yield, biomass and N uptake, as well as ammonia volatilization and leaching loss, were observed. The amount of N sequestered by plants ranged from 3.2 to 3.8 and 9.9 to 10.0 kg N Mg −1 to produce fresh tomato fruits and garlic bulbs, respectively. However, about half of the N for tomato and 93% for garlic plants were removed from soil by harvesting. The N sequestered per unit biomass (or yield) production appears a stable parameter, which can be used to reliably project fertilization needs that target high NUE and minimal loss to the environment.

Estimating the yield of trees is important to improve orchard management and production. Usually, farmers need to estimate the yield of trees at the early growing stage for field management. However, methods to predict the yield at the individual tree level are currently not available because of the complexity and variability of each tree. Thus, in this article, the authors evaluated the performance of an unmanned aerial vehicle (UAV)-based remote sensing system and machine learning (ML) approaches for yield estimation. A multispectral camera was mounted on the UAV platform to acquire high-resolution images. Eight features were extracted from the UAV imagery, including normalized difference vegetation index (NDVI), green normalized vegetation index (GNDVI), red-edge normalized difference vegetation index (NDVIre), red-edge triangulated vegetation index (RTVIcore), individual tree canopy size, the modified triangular vegetation index (MTVI2), the chlorophyll index-green (CIg), and the chlorophyll index-rededge (CIre). Then, plant physiology-informed machine learning (PPIML) algorithms were applied with the extracted features to predict the yield at the individual tree level. Results showed that the decision tree classifier had the best prediction performance, with an accuracy of 85%.

Soil fumigation continues to play an important role in soil disinfection, but tools to significantly reduce emissions while providing environmental benefits (e.g., biochar) are lacking. The objective of this study was to determine the effects of biochar products on fumigant 1,3-dichloropropene (1,3-D) and chloropicrin (CP) emissions, their distribution and persistence in soil, nematode control, and potential toxicity to plants in a field trial. Treatments included three biochar products [two derived from almond shells (ASB) at either 550 or 900 degrees C pyrolysis temperature and one from coconut shells (CSB) at 550 degrees C] at 30 and 60 t ha(-1), a surface covering with a low permeability film (TIF), and no surface covering (control). A mixture of 1,3-D (-65%) and CP (-35%) was injected to-60 cm soil depth at a combined rate of 640 kg ha(-1). All biochar treatments significantly reduced emissions by 38-100% compared to the control. The ASB (900 degrees C) at both rates reduced emissions as effectively as the TIF (by 99-100%). Both fumigant emission reduction and residue in surface soil were positively correlated with biochar's adsorption capacity while cucumber germination rate and dry biomass were negatively correlated with residual fumigant concentrations in surface soil. This research demonstrated the potential and benefits of using biochar produced from local orchard feedstocks to control fumigant emissions. Additional research is needed to maximize the benefits of biochar on fumigant emission reductions without impacting plant growth.

Irrigation management has been one of the keys to achieve sustainability and marketability in agriculture, which is estimated to account for over 70% of global water use. The optimal use of water through irrigation is important for the evolution of agriculture. Many progressive growers make irrigation decisions using crop evapotranspiration (ETc). With the advent of Unmanned Aerial Vehicles (UAVs), lightweight sensors, such as thermal camera, can be mounted on the UAVs to take high-resolution images. Compared with satellite imagery, the spatial resolution of the UAV images can be at the centimeter level. Thus, in this article, the authors proposed a reliable individual tree-level irrigation inference system using a small UAV platform and Convolutional Neural Networks (CNNs). A field study was conducted at the USDA-ARS Research Center in Parlier, California to train and test CNN models using images of the pomegranate trees. The pomegranate field was randomly designed into 16 equal blocks to test two irrigation levels, the low irrigation volume (35% and 50% of ETc) and high irrigation volume (75% and 100% of ETc), measured by a weighing lysimeter in the field. Results showed that the trained CNN model could successfully classify the individual tree using the thermal UAV imagery into the targeted irrigation levels. The overall prediction accuracy was around 87%, which showed a state-of-art performance and indicated that UAV thermal imagery could infer the irrigation levels at individual tree level.

Evapotranspiration (ET) estimation is important in precision agriculture water management, such as evaluating soil moisture, drought monitoring, and assessing crop water stress. As a traditional method, evapotranspiration estimation using crop coefficient ( K c ) has been commonly used. Since there are strong similarities between the K c curve and the vegetation index curve, the crop coefficient K c is usually estimated as a function of the vegetation index. Researchers have developed linear regression models for the K c and the normalized difference vegetation index (NDVI), usually derived from satellite imagery. However, the spatial resolution of the satellite image is often insufficient for crops with clumped canopy structures, such as vines and trees. Therefore, in this article, the authors used Unmanned Aerial Vehicles (UAVs) to collect high-resolution multispectral imagery in a pomegranate orchard located at the USDA-ARS, San Joaquin Valley Agricultural Sciences Center, Parlier, CA. The K c values were measured from a weighing lysimeter and the NDVI values were derived from UAV imagery. Then, the authors established a relationship between the NDVI and K c by using a linear regression model and a stochastic configuration networks (SCN) model, respectively. Based on the research results, the linear regression model has an R 2 of 0.975 and RMSE of 0.05. The SCN regression model has an R 2 and RMSE value of 0.995 and 0.046, respectively. Compared with the linear regression model, the SCN model improved performance in predicting K c from NDVI. Then, actual evapotranspiration was estimated and compared with lysimeter data in an experimental pomegranate orchard. The UAV imagery provided a spatial and tree-by-tree view of ET distribution.