Precisely quantifying crop nitrogen content is critical for adopting sustainable nutrient management practices. This study offers a comprehensive analysis of using hyperspectral data to accurately measure area-based nitrogen content (N) in almond trees at the leaf level. We collected spectral data ranging from 400 to 2500 nm of multiple leaves from 190 samples across two orchards spanning two years. Our methodology involves building a hybrid model that merges a physically based model (PROSPECT-PRO) and a data-driven model (multi-output Gaussian process regression), demonstrating exceptional performance in area-based nitrogen prediction, achieving R2 values of 0.54 and an RMSE of 0.03 mg/cm2 for area-based nitrogen sensing. The hybrid method incorporates synthetic spectra produced through principal component analysis (PCA) and labeled with biochemical traits retrieved by PROSPECT-PRO for training and validation, while the real data was kept unseen for testing. We compared the performance of physically based, hybrid, and data-driven models using R2 and NRMSE as metrics. The Partial Least Squares Regression (PLSR) model showed a strong relationship between leaf N and spectral reflectance (R2 = 0.75); however, PLSR is prone to bias from the training set and may perform poorly on unseen data. The findings also highlight the importance of the Short-Wave Infrared region in nitrogen determination, particularly the bands from 2100 to 2200 nm. Additionally, protein content was found to be a more reliable proxy for nitrogen than chlorophyll. By comparing the retrieved leaf traits with ground truth data, we realized that PROSPECT PRO consistently underestimates almond leaf traits such equivalent water thickness (EWT), carbon-based compounds (CBC), and overestimates Nitrogen. Therefore, adjustment factors were determined for these traits that are estimated with PROSPECT-PRO.

Boron (B) is an essential and widely studied element in plants. Due to B dynamics in highly weathered soils, its concentration is generally low. Among other benefits, B interacts with calcium pectate, promotes stability on cellular membrane, and influences directly on plant nutrients uptake and non-structural metabolites synthesis. In sugarcane (Saccharum spp.) crop, adequate B supply has been associated with juice quality and yield of stalks and sugar and its response on adequate B concentration on commercial fields can differ greatly even into a group of varieties recommended for the same production environment. In this context, the authors aimed to assess the effects of B availability on sugarcane root and shoot development, nutrient status, and carbohydrate synthesis and allocation in two sugarcane varieties recommended for the same production environment using hydroponic solution. The experimental design was completely randomized and consisted of four treatments and four replicates. The treatments comprised two sugarcane varieties (RB867515 and RB92579) and two B concentrations (0.05 and 0.5 mg L−1) considered deficient and adequate, respectively, for plant development. Carbohydrate partitioning, nutrient concentrations in various plant parts, and growth and morphological parameters were evaluated. Under adequate B supply, the total concentrations of reducing sugars and sucrose increased 67 and 20% in RB867515 and 30 and 20% in RB92579, respectively, whereas starch decreased by 27% for both varieties. Adequate B supply increased the concentrations of all elements in all plant organs, except for N and K in leaves, and improved most yield and morphological parameters. Principal component analysis correlated the higher carbohydrates concentration and yield parameters with the variety RB92579, whereas the highest concentration of most nutrients was mainly associated with the variety RB867515, especially under adequate B supply. The main influence of adequate B supply was on carbohydrate synthesis. Although the sugarcane varieties responded differently to B availability, their biometric parameters were enhanced by adequate B supply. These results emphasize the need for B fertilization, regardless of the sugarcane variety’s susceptibility to B deficiency.

Climate change is a major threat to crop potential and is characterized by both long-term shifts in temperature and precipitation patterns as well as increased occurrence of extreme weather events, these extreme weather events are the most immediate and intractable threat to agriculture. Crop resilience in the face of stress depends upon the speed and effectiveness with which plants and cropping systems sense and respond to that stress. A variety of agronomic practices including breeding, exogenous inputs (nutrients, water, biostimulants and others) and shifts in cultivation practice have been used to influence plant stress response to achieve the goal of increased plant and cropping system resilience. Traditional breeding is a powerful tool that has resulted in stable and long-term cultivar improvements but is often too slow and complex to meet the diverse, complex and unpredictable challenges of climate induced stresses. Increased inputs (water, nutrients, pesticides etc.) and management strategies (cropping system choice, soil management etc.) can alleviate stress but are often constrained by cost and availability of inputs. Exogenous biostimulants, microbials and plant hormones have shown great promise as mechanisms to optimize natural plant resilience resulting in immediate but non-permanent improvements in plant responses to climate induced stresses. The failure to modernize regulatory frameworks for the use of biostimulants in agriculture will constrain the development of safe effective tools and deprive growers of means to respond to the vagaries of climate change. Here we discuss the scientific rationale for eliminating the regulatory barriers that constrain the potential for biostimulants or products that modulate plant regulatory networks to address climate change challenges and propose a framework for enabling legislation to strengthen cropping system resilience.

Recycling almond hulls and shells as organic matter amendments in orchards can be used as a strategy to increase the soil microbial biomass of many functional community groups that metabolize a variety of different carbon substrates. In an almond orchard field trial, hull/shell amendments were applied to the soil surface with a compost spreader and remained undisturbed over time. The organic hull/shell layer supported high living microbial biomass and led to significant improvements in the soil microbial community beneath it. In the upper soil layer, soil bacteria were significantly higher under the amendments (mean of 47.6 ng bacterial biomass g-1 soil) compared to control soil (mean of 40.3 ng g-1) after one year. After a year and a half without soil disturbance, soil fungi were significantly higher under amendments (mean of 3014.9 ng g-1) compared to control soil (mean of 1483.3 ng g-1). The resulting soil microbial community included higher saprophytes, arbuscular mycorrhizal fungi, gram(-) and gram(+) bacteria, and actinomycetes than control soil, thus increasing microbial multifunctionality. Community-level physiological profiling demonstrated the amendment increased soil microbial activity in response to a range of carbon sources such as amino acids, carbohydrates, carboxylic acids, and polymers. The organic hull/shell layer itself supported high total microbial biomass across trophic levels, including protozoa. The amendment decomposed by 45 % of its initial dry weight in the first year and the carbon to nitrogen ratio (C:N) declined from 53:1 to 29:1. No effects of microbial nitrogen immobilization on tree nitrogen status were found and yields between treatments were similar. The organic layer created beneficial conditions for tree root growth, leading to higher almond root biomass density (means of 0.85 and 1.7 mg dry roots cm -3 in the control and amended soils, respectively) after a year and a half. Maintaining hull/shell amendments on the soil surface over time created a beneficial organic layer that promoted many functional microbial groups and nutrient cycling in the organic and upper soil layers.

Nitrogen (N) is vital for plant growth, but its imbalance can negatively affect crop yields, the environment, and water quality. This is especially crucial for California’s almond orchards, which are the most N-hungry nut crop and require substantial N for high productivity. The current practices of uniform and extensive N application lead to N leaching into the groundwater, creating environmental hazards. Traditional remote sensing methods often rely on data-driven approaches that work well statistically (achieving a high R2 value) with one dataset but aren’t adaptable across different datasets. To create a more robust, data-driven model, one would typically need a vast and varied collection of datasets. Our goal, however, is to develop a more universally applicable model using smaller datasets, typical of commercial orchards, that can accurately estimate N content in tree canopies, regardless of differences in spatial, spectral, and temporal data. In this study, we investigate and evaluate multiple remote sensing approaches for estimating N concentration in Californian almonds, utilizing hyperspectral imaging at the canopy level. We assess various classical vegetation indices, machine learning models, and a physics-informed 3D radiative transfer model. While cross-validated results show comparable results for radiative transfer models and best-performing machine learning models, most single vegetation indices are not capable of exceeding the baseline model f(𝐱)=y̅ and thus had R2 value less than 0. Despite being less commonly used, 3D radiative transfer modeling shows promise as a strong and adaptable method, producing results that are comparable to the best machine learning models.

Adding organic amendments to soils in orchards has been suggested as a climate-smart agricultural practice that can increase resilience to extremes such as drought. The benefits of adding almond hulls and shells to soil include releasing potassium into the soil, improving water infiltration, reducing soil water evaporation, and enhancing water-holding capacity. In this study, soil infiltration and root water uptake (RWU) patterns of almond trees in amended and control soil treatments were investigated. An almond orchard was mulched with a mixture of almond hulls and shells used as surface-applied organic matter amendments. The combined use of time-lapse electrical resistivity tomography (ERT), stem water potential (SWP) and leaf water potential (LWP) measurements were used to evaluate RWU, soil infiltration patterns, and tree water status at different times during the study. The results of the ERT showed that the RWU patterns for the almond trees are distinct between the amended and the control treatments after the irrigation was applied. Compared to the control treatment, the amended treatment allowed the soil to store more water. Regardless of treatment, the majority of RWU patterns were observed in the top 0.5-1 m root zone depth. Almond trees began to recover from stress, as indicated by the SWP and LWP values, four hours after the start of irrigation. In addition, this was demonstrated by the ERT measurements, which revealed the RWU activity four hours after the irrigation had been applied. This research reveals that time-lapse ERT surveys combined with soil and tree water status data can infer patterns of RWU in almond trees grown with organic soil amendments and under controlled conditions. Furthermore, it was concluded that using almond hulls and shells as organic matter amendments can help almond growers improve infiltration, making almond production more resilient to climate change-related extremes such as droughts.

Using compost as an agricultural amendment offers a means to reduce organic waste, as mandated in the US state of California (SB 1383). Compost can improve soil physical characteristics and soil carbon content by increasing soil organic matter. We examined the effect of compost application (7-year duration) on soil nitrous oxide (N2O) emissions, inorganic nitrogen (N) pools, soil temperature and water content, bulk density, and total carbon (C) and N content. The orchard was composed of Nonpareil cultivars interplanted with Aldrich and Carmel cultivars, all grafted on Nemaguard peach rootstock [Prunus persica (L.) Bratsch]. Its soil was Oakdale sandy loam (coarse-loamy, mixed, active, thermic Mollic Haploxeralf). Delivering nutrients through the irrigation system, the orchard was fertigated 14 times with urea ammonium nitrate or calcium ammonium nitrate, using high frequency and low concentration (HFLC) fertigation applications (total 195 kg N ha(-1)). Soil without compost (No Compost) tended to have higher N2O fluxes (up to 2.75-fold) than soil with compost (Compost). Cumulative measured N2O emissions (i.e., sum of nine sampling events) from Compost were half that from No Compost. Soil pH, electrical conductivity, total C, and N tended to be greater in Compost, and bulk density tended to be lower in Compost than No Compost. No relationships between N2O emissions and soil temperature, volumetric water content, water-filled pore space, and inorganic N pools occurred in either treatment. Our findings indicate that long-term applications of compost in perennial crops, in combination with an HFLC nutrient management program, could reduce losses of N as N2O to the atmosphere.

Hulls and shells are an abundant by-product from almond production with potential as an organic matter amendment (OMA). A combination of incubation study and field research was conducted in 2019–2021 to evaluate the impacts of three practices in combination on orchard soils’ C and N cycling, including a 210-day period of laboratory incubation with hulls and shells, and field sampling of orchard soils with and without historic applications of green waste compost as an OMA; with hulls and shells and with and without off-ground harvest where orchard soils remain undisturbed year round. Hulls and shells increased microbial biomass carbon in the field study by 248 μg g−1 dry soil after one year (p < 0.001) and during incubation, and increased cumulative respiration in soils with and without historic OMA (p < 0.001). Historic OMA resulted in double the total soil organic carbon (SOC) and total nitrogen (TN) compared to soil without resulting in significantly higher respiration and N mineralization when amended with hulls and shells. The decomposition of hull and shell biomass following surface application progressed at similar rates in the laboratory and field (1.7 g kg−1 d−1 during incubation (R2 = 0.84) and 1.3 g kg−1 d−1 in the field trial (R2 = 0.91). Our results highlight the suitability of hulls and shells as a by-product source of OMA for improving soil health in orchards with historic OMA and transitioning to organic matter management.

This study investigates the impact of alternative fertilization practices on the yield-scaled global warming potential (YS-GWP) in almond orchards. Almond production is a contributor to greenhouse gas emissions, primarily due to nitrogen-based mineral fertilizers. This research aims to identify strategies that reduce the environmental footprint of almond cultivation while maintaining yield. Field experiments were conducted in an almond orchard using three alternative fertigation practices: Advance Grower Practice (AGP), Pump and Fertilize (P&F), and High Frequency Low Concentration (HFLC). AGP is the current practice used by producers to meet annual N demand for almond tree growth; P&F is a reduction in applied N rate in response to measured N concentrations in the groundwater so that the added N and groundwater N reach the same total N applied; HFLC is a practice of applying smaller N rates per individual event. HFLC uses a greater number of fertigation events to reach similar total annual N load as other treatments. Cumulative N2O and CH4 emissions were used to determine GWP by converting the emissions to carbon dioxide equivalents (CO2eq) within a 100-year horizon. Nitrous oxide emissions were multiplied by a radiative forcing potential CO2eq of 298 and CH4 by 25 (UNFCCC, 2007). The results revealed that both P&F and HFLC reduced the YS-GWP compared to AGP. HFLC demonstrated 52–78% decrease in GWP per unit of almond yield compared to AGP, while P&F showed 48–58% decrease over AGP. These reductions were attributed to the reduced nitrous oxide emissions associated with P&F and HFLC. Further, P&F and HFLC tended to have higher N use efficiency than AGP. We demonstrate that adopting alternative fertilization practices can effectively mitigate the environmental footprint of almond orchards while maintaining crop yields. These practices offer viable options for almond growers to reduce greenhouse gas emissions, enhance sustainability, and contribute to climate change mitigation.

Since soybean is widely cultivated around the world and has a high protein content, it is a great nutritional vehicle for increasing the dietary uptake of selenium (Se). Several studies have evaluated biofortification with Se through fertilizer application in several crops. However, it is not clear how each method and source affect the total Se content or Se species in soybean grains. This work aimed to assess the total Se content and Se speciation in Se-enriched soybean grains produced under different Se application methods in the field. The treatments consisted of Se application (soil or foliar), using organic or inorganic Se sources at 10 g ha(-1) or 80 g ha(-1), in two genotypes. The results showed that all treatments with inorganic Se (soil and foliar) increased the Se content in grains compared with the control. More than 80% of the total Se in grains was present as selenomethionine (SeMet), and the speciation was affected by the Se source and the method of application. The treatments using inorganic Se, applied via soil or foliar, produced the highest content of Se as SeMet in soybean grains. Finally, we propose that the preservation of the Se species in products derived from soybean grains be evaluated as the following step.

Achieving sustainability in agricultural nitrogen (N) management relies on farmers’ decisions to reduce fertilizer inputs and adopt conservation management practices. Understanding the drivers and barriers to farmers’ adoption of improved N management practices is critical to developing effective management and policy approaches on this intractable challenge. Existing research on farmer behavior has assumed that any barrier to adoption will result in lower practice adoption rates, without fully understanding how barriers may vary across different management practices, farm and farmer types, and stages of adoption. By leveraging two farmer survey data sets (total n > 1,900), this study diagnoses key barriers to adoption across 11 different N management practices and a large diversity of farmer and farm types across the California Central Valley. We find resource constraints, technical knowledge, and uncertainty emerge as key barrier types that differentially affect farmers at various stages of adoption. On a practice-by-practice basis, uncertainty barriers appear greatest for nonadopters of a practice, whereas practice adopters are more likely to report resource barriers. Across management practices at the farm level, farmers with higher self-reported conservation orientations are more likely to report being affected by all barrier types, as compared to their peers with lower self-reported conservation orientations. Our findings demonstrate that barriers to adoption are more complex than simply the factors that predict lower adoption, as both adopters and nonadopters experience barriers. Furthermore, factors that typically predict higher adoption, such as conservation motivation, do not insulate a farmer from facing barriers to adoption. We consider how adopters are likely to go through a learning process while moving from considering to fully implementing a new practice, during which different barriers to behavior change may be encountered. We argue that interventions intended to motivate farmer adoption of improved management practices need to take more nuanced approaches to understanding how barriers to adoption are likely to vary across stages of adoption, farm and farmer type, and specific management practices.

Pistachio (Pistacia vera L.), an economically and nutritionally important tree crop, relies on winter chill for bud endodormancy break and subsequent blooming and nut production. However, insufficient winter chill poses an increasing challenge in pistachio growing regions. To gain a better understanding of the physiological and biochemical responses of endodormant pistachio buds to chilling accumulation, we investigated the global gene expression changes in flower buds of pistachio cv. Kerman that were cultivated at three different orchard locations and exposed to increasing durations of winter chill. The expression of genes encoding β-1,3-glucanase and β-amylase, enzymes responsible for breaking down callose (β-1,3-glucan) and starch (α-1,4-glucan), respectively, increased during the endodormancy break of pistachio buds. This result suggested that the breakdown of callose obstructing stomata as well as the release of glucose from starch enables symplasmic trafficking and provides energy for bud endodormancy break and growth. Interestingly, as chilling accumulation increased, there was a decrease in the expression of nine-cis-epoxycarotenoid dioxygenase (NCED), encoding an enzyme that uses carotenoids as substrates and catalyzes the rate-limiting step in abscisic acid (ABA) biosynthesis. The decrease in NCED expression suggests ABA biosynthesis is suppressed, thus reducing inhibition of endodormancy break. The higher levels of carotenoid precursors and a decrease in ABA content in buds undergoing endodormancy break supports this suggestion. Collectively, the temporal transcriptome and biochemical analyses revealed that the degradation of structural (callose) and non-structural (starch) carbohydrates, along with the attenuation of ABA biosynthesis, are critical processes driving endodormancy break in pistachio buds.

Background Crop residues used as organic matter amendments have been shown to release potassium (K) into the soil, promoting K cycling in agronomic systems. Orchard field trials are needed to evaluate K dynamics under almond hull and shell amendments, which contain high K concentrations. Methods Three field trials in commercial almond orchards were conducted to assess the effects of surface-applied almond hull and shell amendments on K cycling within plant and soil systems. Amendment K concentrations over time, soil exchangeable K, and tree K status were measured as well as decomposition rate and crop yield. Results Hulls and shells released K rapidly under irrigation and rainfall, significantly increasing soil exchangeable K in the upper 0–10 cm soil within 2–7 weeks. Amendments increased tree leaf K status within the first 1–3 years to varying degrees depending on site. Initial amendment K concentrations decreased by at least half by dry weight within the first 25.4 cm (10 inches) of water (irrigation and precipitation) within the irrigated zone. Conclusions Almond hulls and shells can increase soil and plant K status when used as amendments on the soil surface. This practice can address byproduct utilization issues, recycle potassium (K), and reduce orchard K fertilizer demand by replacing the majority of tree K demand. Growers can tailor application rates to meet orchard-specific K management goals. Off-ground harvest preserved the hull/shell organic layer over time and maximized K cycling. Hull/shell amendments applied on the soil surface cover more soil area within the irrigated wetted zone compared to banded K fertilizer. This practice can reduce reliance on K fertilizers and reduce associated costs while providing a convenient outlet for hulls and shells.

IntroductionEstimating and understanding the yield variability within an individual field is critical for precision agriculture resource management of high value tree crops. Recent advancements in sensor technologies and machine learning make it possible to monitor orchards at very high spatial resolution and estimate yield at individual tree level.MethodsThis study evaluates the potential of utilizing deep learning methods to predict tree-level almond yield with multi-spectral imagery. We focused on an almond orchard with the ‘Independence’ cultivar in California, where individual tree harvesting and yield monitoring was conducted for ~2,000 trees and summer aerial imagery at 30cm was acquired for four spectral bands in 2021. We developed a Convolutional Neural Network (CNN) model with a spatial attention module to take the multi-spectral reflectance imagery directly for almond fresh weight estimation at the tree level.ResultsThe deep learning model was shown to predict the tree level yield very well, with a R2 of 0.96 (±0.002) and Normalized Root Mean Square Error (NRMSE) of 6.6% (±0.2%), based on 5-fold cross validation. The CNN estimation captured well the patterns of yield variation between orchard rows, along the transects, and from tree to tree, when compared to the harvest data. The reflectance at the red edge band was found to play the most important role in the CNN yield estimation.DiscussionThis study demonstrates the significant improvement of deep learning over traditional linear regression and machine learning methods for accurate and robust tree level yield estimation, highlighting the potential for data-driven site-specific resource management to ensure agriculture sustainability.

Potassium (K) in fruit and nut orchards affects productivity, water relations, and crop quality; however, information about K, calcium (Ca), and magnesium (Mg) dynamics in orchard soils is limited. This study evaluates the distribution of K, Ca, and Mg in 30-cm soil columns following four wetting events over 70 days. Soils were a Hillgate sandy loam, a San Ysidro loam, and a Yolo loam applied with muriate of potash (KCl), microfine sulfate of potash (SOP; [K2SO4]), polyhalite (POLY; [K2SO4 center dot MgSO4 center dot 2CaSO(4)center dot 2H(2)O]), and an unfertilized control. Soil solution K concentrations measured at 5 cm using a porewater sampler were greater than 1400 mg K L-1 in the Hillgate sandy loam POLY treatment after the first two wetting events. Ca and Mg in soil solution were also greater for POLY when averaged across all four wetting events, likely due to greater Ca and Mg inputs. Extractable K using ammonium acetate, significantly increased after the wetting events resulting in 88% and 75% of the extractable K in the top 0-10 cm of the San Ysidro and Yolo loams, respectively. Leached K accounted for 6.2% of the SOP inputs in the Hillgate sandy, whereas only 0.3% and 1.1% in the San Ysidro and Yolo loams, respectively. Higher K leaching as well as Ca and Mg is likely due to higher cation exchange capacity in loam soils and great porosity in sandy loam soils. Our results show differences in the distribution of K, Ca, and Mg and provide insights into K fertilizer availability for orchard soils.

Tracking plant water status is a critical step towards the adaptive precision irrigation management of processing tomatoes, one of the most important specialty crops in California. The photochemical reflectance index (PRI) from proximal sensors and the high-resolution unmanned aerial vehicle (UAV) imagery provide an opportunity to monitor the crop water status efficiently. Based on data from an experimental tomato field with intensive aerial and plant-based measurements, we developed random forest machine learning regression models to estimate tomato stem water potential (ψstem), (using observations from proximal sensors and 12-band UAV imagery, respectively, along with weather data. The proximal sensor-based model estimation agreed well with the plant ψstem with R2 of 0.74 and mean absolute error (MAE) of 0.63 bars. The model included PRI, normalized difference vegetation index, vapor pressure deficit, and air temperature and tracked well with the seasonal dynamics of ψstem across different plots. A separate model, built with multiple vegetation indices (VIs) from UAV imagery and weather variables, had an R2 of 0.81 and MAE of 0.67 bars. The plant-level ψstem maps generated from UAV imagery closely represented the water status differences of plots under different irrigation treatments and also tracked well the temporal change among flights. PRI was found to be the most important VI in both the proximal sensor- and the UAV-based models, providing critical information on tomato plant water status. This study demonstrated that machine learning models can accurately estimate the water status by integrating PRI, other VIs, and weather data, and thus facilitate data-driven irrigation management for processing tomatoes.

This review gathered and analyzed data about (i) the Cd-induced impacts on seed germination and seedling vigor, and (ii) the use of different priming agents to mitigate Cd-induced impacts on the early plant development. Critical evaluation of the obtained data revealed intriguing results. First, seeds of diverse species can endure exposures to Cd. Such endurance is exhibited as maintenance of or even improvement in the seed germination and vigor (up to 15% and 70%, respectively). Second, the main factors influencing seed tolerance to Cd toxicity are related to temporal variations in anatomical, physiological, and/or biochemical features. Third, Cd can trigger diverse transgenerational effects on plants by shaping seed endophytes, by modulating seed provisioning with resources and regulatory elements, and/or by altering seed (epi)genomics. Fourth, different chemical, biological and physical priming agents can mitigate Cd-induced impacts on seeds, sometimes enhancing their performance over the control (reference) values. Overall, this review shows that the impacts of Cd on seed germination and vigor encompass not only negative outcomes but also neutral and positive ones, depending upon the Cd dose, media properties, plant species and genotypes, plant developmental stage and organ, and management approaches. Increasing our understanding of plant tolerance mechanisms against the growing background Cd pollution is relevant to support breeding programs, agricultural practices, and health-environmental policies.