Defining quantitative soil health goals can support efforts to improve soil quality and meet broader ecosystem services goals, while simultaneously helping field-level benchmarking of soil health on farms. But soil health metrics in agricultural systems require edaphic context, notably climate, soil type (soil texture and classification), as well as cropping system. Soil samples (n=1,328) from New York State (USA) with Land Resource Regions (LRR), texture, and cropping system information were analyzed for eight physical and biological soil health indicators (soil organic matter, permanganate-oxidizable carbon, respiration, protein, available water capacity, wet aggregate stability, and penetration resistance from 0-15 and 15-45 cm), and population distribution functions were determined. Production environment soil health (PESH) goals were derived for four soil texture groups and six cropping systems by proposing the 75th and 90th percentile for each factorial class. Finer-textured soils and Pasture and Mixed Vegetable cropping systems generally had the highest values for soil health goals, followed by Dairy Crop and Orchard systems, then Annual Grain, and lastly Processing Vegetable systems. Long Island (LRR-S) had soil organic matter PESH goals that were on average 0.7 % lower than the rest of New York State (LRRs-L&R). This implies that regional PESH goals within a state or region may be warranted if edaphic context is considerably different.
Surface water nutrient pollution, the primary cause of eutrophication, remains a major environmental concern in Western Lake Erie despite intergovernmental efforts to regulate nutrient sources. The Maumee River Basin has been the largest nutrient contributor. The two primary nutrients sources are inorganic fertilizer and livestock manure applied to croplands, which are later carried to the streams via runoff and soil erosion. Prior studies on nutrient source attribution have focused on large watersheds or counties at long time scales. Source attribution at finer spatiotemporal scales, which enables more effective nutrient management, remains a substantial challenge. This study aims to address this challenge by developing a portable network model framework for phosphorus source attribution at the subwatershed (HUC-12) scale. Since phosphorus release is uncertain, we combine excess phosphorus derived from manure and fertilizer application and crop uptake data, flow dynamics simulated by the SWAT model, and in-stream water quality measurements into a probabilistic framework and apply Approximate Bayesian Computation to attribute phosphorus contributions from subwatersheds. Our results show significant variability in subwatershed-scale phosphorus release that is lost in coarse-scale attribution. Phosphorus contributions attributed to the subwatersheds are on average lower than the excess phosphorus estimated by the nutrient balance approach adopted by environmental agencies. Phosphorus release is higher during spring planting than the growing period, with manure contributing more than inorganic fertilizer. By enabling source attribution at high spatiotemporal resolution, our lightweight and portable model framework is suitable for broad applications in environmental regulation and enforcement for other regions and pollutants.
The Central Highlands of Vietnam is the biggest Robusta coffee (Coffea canephora Pierre ex A.Froehner) growing region in the world. This study aims to identify the most important climatic variables that determine the current distribution of coffee in the Central Highlands and build a “coffee suitability” model to assess changes in this distribution due to climate change scenarios. A suitability model based on neural networks was trained on coffee occurrence data derived from national statistics on coffee-growing areas. Bias-corrected regional climate models were used for two climate change scenarios (RCP8.5 and RCP2.6) to assess changes in suitability for three future time periods (i.e., 2038-2048, 2059-2069, 2060-2070) relative to the 2009-2019 baseline. Average expected losses in suitable areas were 62% and 27% for RCP8.5 and RCP2.6, respectively. The loss in suitability due to RCP8.5 is particularly pronounced after 2060. Increasing mean minimum temperature during harvest (October-November) and growing season (March-September) and decreasing precipitation during late growing season (July-September) mainly determined the loss in suitable areas. If the policy commitments made at the Paris agreement are met, the loss in coffee suitability could potentially be compensated by climate change adaptation measures such as making use of shade trees and adapted clones.
Scoring plant phenotypes across large populations in multiple environments is a necessary precondition to both using natural genetic diversity to build genotype to phenotype models, study genotype by environment interactions and to carry out plant breeding to develop high yielding and more resilient cultivars. Here we explore data driven approaches using latent representations of leaf reflectance data collected from a large field experiment consisting of a subset of diverse maize lines drawn from the Wisconsin diversity panel (Mazaheri et al., 2019). In this experiment, 2 replicates of 752 inbred lines from the Wisconsin diversity panel were grown in field conditions. An ASD spectrometer was used to collect data on intensity of light reflected by leaves at 1 nanometer wide intervals between350 to 2,500 nm, resulting in a total of 2,151 reflectance intensity values measured for each plot. Two dimensional reduction approaches were evaluated for this dataset: conventional principal component analysis and an auto-encoder based neural network. Ten principal components were sufficient to summarize 99% of variance in the dataset. An autoencoder neural network comprising of an encoder having three dense layers and a decoder having four dense layers was able summarize variation within the dataset at a validation loss of 0.006 using 10 latent variables. A number of principal components and latent variables were correlated with several phenotypes quantified for a subset of the same field grown research plots (Figure 2A;2C). Chlorophyll, the major photosynthetic pigment in plant leaves, plays a substantial role in determining the overall pattern of reflectance for maize leaves. The abundance of chlorophyll was significantly correlated with PC2 (R2 = 0.31) (Figure 2B) which explained 11% of the total variance in higher spectral reflectance data. However, autoencoder based summary of the same trait dataset appears to have more accurately captured variation in chlorophyll abundance within this field trial with LV8 exhibiting a R2 = 0.59 (Figure 2D) with ground truth chlorophyll measurements. Both PCA and autoencoder based dimensional reduction captures a mix of variables which were heritable (i.e. a large proportion of total variance was attributable to differences between genotypes) and variables that were not heritable. Two of ten PCs evaluated exhibited H2 values >0.5 as did four of ten latent variables generated (Figure 3A; 3B). Genome wide association studies (GWAS) conducted using high heritability principal components and latent variables identified significant signals in 2 out of 6 cases (Figure 4A; 4B). Ongoing work is needed to evaluate the potential of using candidate genes underlying GWAS peaks to assign putative biological roles to latent variables estimated from raw sensor data by autoencoders or other dimensional reduction approaches.
In a model simulating dynamics of a system, parameters can represent system sensitivities and unresolved processes, therefore affecting model accuracy and uncertainty. Taking a light use efficiency (LUE) model as an example, which is a typical approach to estimate gross primary productivity (GPP), we propose a Simultaneous Parameter Inversion and Extrapolation approach (SPIE) to overcome issues stemming from plant-functional-type(PFT)-dependent parameterizations. SPIE refers to predicting model parameters using an artificial neural network based on collected variables, including PFT, climate types, bioclimatic variables, vegetation features, atmospheric nitrogen and phosphorus deposition and soil properties. The neural network was optimized to minimize GPP errors and constrain LUE model sensitivity functions. We compared SPIE with 11 typical parameter extrapolating methods, including PFT- and climate-specific parameterizations, global and PFT-based parameter optimization, site-similarity, and regression approaches. All methods were assessed using Nash-Sutcliffe model efficiency(NSE), determination coefficient and normalized root mean squared error, and contrasted with site-specific calibrations. Ten-fold cross-validated results showed that SPIE had the best performance across sites, various temporal scales and assessing metrics. None of the approaches performed similar to site-level calibrations(NSE=0.95), but SPIE was the only approach showing positive NSE(0.68). The Shapley value, layer-wise relevance and partial dependence showed that vegetation features, bioclimatic variables, soil properties and some PFTs are determining parameters. The proposed parameter extrapolation approach overcomes strong limitations observed in many standard parameterization methods. We argue that expanding SPIE to other models overcomes current limits and serves as an entry point to investigate the robustness and generalization of different models.
Current soil C inventories focus on surface layers although over half of soil C is found below 20 cm. Recent and ongoing changes in agricultural management, crop productivity, and climate in Midwest US cropland may influence subsoil C stocks. The objectives of this study were to determine how surface soil and subsoil organic C stocks have changed in croplands of Iowa and Illinois and to evaluate mechanisms to explain the observed subsoil organic C changes. Using resampling studies from Iowa and Illinois, we found that subsoil (20-80 cm) organic C increased at a rate of 0.31 Mg C ha-1 yr-1 between the 1950s and early 2000s despite C losses of similar magnitude in the top 20 cm (0.26 Mg C ha-1 yr-1). Based on this analysis, we estimated a subsoil C storage rate of up to 11.8 Tg C yr-1 for Iowa and Illinois, which equates to 12% of annual US greenhouse gas emissions from crop cultivation if surface C losses and non-CO2 greenhouse gases are controlled. We also measured changes in soil organic C stocks from two long-term cropping systems experiments located in Iowa, which demonstrated similar rates of subsoil C changes for both historical and contemporary crop rotations. Using publicly available crop yield data, we determined that changes in crop productivity likely contributed minorly to observed changes in subsoil organic C. The accumulation of organic C in subsoils may be attributed to regional climate change, which has led to greater precipitation and wetter subsoils that inhibit transformation of soil organic C to CO2. Because farmers may respond to increasing soil wetness by expanding and intensifying artificial drainage infrastructure, there is an urgent need to further assess subsoil C stocks and their vulnerability to drainage system changes.
Earth Observations (EO) systems aim to monitor nearly all aspects of the global Earth environment. Observations of Essential Water Variables (EWVs) together with advanced data assimilation models, could provide the basis for systems that deliver integrated information for operational and policy level decision making that supports the Water-Energy-Food-Nexus (EO4WEF), and concurrently the UN Sustainable Development Goals (SDGs), and UN Framework Convention on Climate Change (UNFCCC). Implementing integrated EO for GEO-WEF (EO4WEF) systems requires resolving key questions regarding the selection and standardization of priority variables, the specification of technologically feasible observational requirements, and a template for integrated data sets. This paper presents a concise summary of EWVs adapted from the GEO Global Water Sustainability (GEOGLOWS) Initiative and consolidated EO observational requirements derived from the GEO Water Strategy Report (WSR). The UN-SDGs implicitly incorporate several other Frameworks and Conventions such as The Sendai Framework for Disaster Risk Reduction; The Ramsar Convention on Wetlands; and the Aichi Convention on Biological Diversity. Primary and Supplemental EWVs that support WEF Nexus & UN-SDGs, and Climate Change are specified. The EO-based decision-making sectors considered include water resources; water quality; water stress and water use efficiency; urban water management; disaster resilience; food security, sustainable agriculture; clean & renewable energy; climate change adaptation & mitigation; biodiversity & ecosystem sustainability; weather and climate extremes (e.g., floods, droughts, and heat waves); transboundary WEF policy.
A study was conducted in none tilled coffee agroforestry fields of Eastern Uganda to understand the effects of application of inorganic fertilizers on soil nutrient loss in form of gas for mitigation of unsustainable agricultural practices. This study specifically i) assessed the effect of application of inorganic fertilizers on greenhouse gas emissions, ii) determined their effect on microbial carbon, nitrogen and phosphorus and iii) determined their effect on leaf litter decomposition under Albizzia-coffee growing systems of the Mount Elgon. Soil gas emissions were measured with the static chamber method for twelve months in a field experiment with five different fertilizer treatments. The effect of treatments was separated using ANOVA in Genstat discovery version 13. Microbial carbon, nitrogen and phosphorus was separated using Mann-Whitney U test. Results showed that annual emissions ranged from 19.6 to 26.1 (t C/ha/yr), 3.5 to 9 (Kg N/ha/yr) and 6.9 to 9.2 (Kg C/ha/yr) for carbon dioxide, nitrous oxide and methane respectively. Significant effects on soil emissions only occurred for nitrous oxide (P=0.017), microbial carbon (p=0.001) and microbial phosphorus (p<0.001) for the study period. The mixture of NPK fertilizers presented the lowest carbon dioxide loss and application of TSP presented the lowest nitrous oxide emission from soil. This study underscores the need for establishment of long-term experiments across several agro-ecological zones to confirm farmers’ perceptions of their soil fertility levels and ascertain the contribution of farm practices towards the retention of nutrients in the soil with minimal emission, to inform decisions of small holder farmers, policy and development partners for sustainable production.
Whole plant chlorophyll fluorescence imaging is a powerful tool for non-destructive analysis of photosynthesis. Analysis of such images requires software that is able to process and calculate photosynthetic parameters per plant pixel. PlantCV is an open-source, Python-based library of image analysis tools for plant science. Previous versions of PlantCV included tools to analyze photosynthetic efficiency data, but recent developments to the photosynthesis subpackage have expanded to include more photosynthetic parameters based on chlorophyll fluorescence and spectral indices. This paper highlights the newest updates to the photosynthesis package of PlantCV and discusses applications of these tools on a sorghum dataset that was imaged with a PhenoVation CropReporter system.
Multi-plant imaging using arrays of low-cost cameras is a successful strategy for capturing affordable high-throughput plant phenotyping data. An imaging platform of this type can enable simultaneous imaging of hundreds to thousands of plants. The resulting datasets enable analysis of dynamic plant growth, development, and environmental responses at high temporal resolution. Full analysis of these datasets requires the identification of individual plants for measurement, but computational separation of individual plants becomes challenging when neighboring plants overlap. Here, we introduce the use of the watershed transform to segment moderately overlapping plants in multi-plant time series datasets. Rather than focusing on segmenting plants in individual images, we utilize information encoded in the entire time series to propagate plant labels from an early time point when individual plants are separate to later time points. In preliminary studies, using this method allowed us increase the analyzable size of the dataset by 28%.
High-throughput phenotyping and genotyping have provided a vast source of information for evaluating the genetic merit of different breeding materials, but their implementation has been limited in alfalfa due to the complexity of the genome and the perennial nature of the crop. Vegetative indices (VIs) collected from an unmanned aerial vehicle (UAV) equipped with multi-spectral camera can be used to study forage growth and development throughout each growth cycle. Random regression models could be implemented to fit such longitudinal phenotypes like VIs collected over time to estimate growth curves, to access genetic variation in growth and the relations of VIs to end-use traits like forage yield and quality. The main objectives of this project are (1) to incorporate aerial high-throughput phenotyping to predict performance and genetic merit of the breeding materials, (2) to fit longitudinal random regression model to estimate genotype-specific growth curves, and (3) to develop a genotyping approach to estimate genetic relationships between alfalfa populations. The imaging of the alfalfa experimental trials was done every ~ 4.3 days throughout the growing season. The Vegetative indices (VIs) close to the harvest date were extracted and used to fit multi-traits models to evaluate the genetic correlations between VIs and forage biomass yield. The VIs considered were Normalized Vegetative index (NDVI), Green NDVI, Red Edge NDVI, simple ratio of Near Infrared to Red (NIR), and Digital Surface Map (DSM). The preliminary results showed highest correlation of Green NDVI and biomass yield (0.4053, 0.7875, and 0.6779), followed by Rededge NDVI and biomass yield (0.417, 0.7898, and 0.6417) for the first, second and third cuttings respectively for the experimental trial located at Helfer, Ithaca. Heritability estimates ranging from 0.03 to 0.75 was observed indicating the presence of genetic variation in these VIs. Pairwise Fst values estimated from population-level genotyping approach was found to be efficient estimates of genetic relatedness between populations. Random regression models with a linear spline function and legendre polynomials including other environmental trials are under evaluation to see the potentiality of these models to fit VIs from multiple time points.
In the United States, voluntary and compliance carbon markets are being created there is an effort to match producers of carbon credits with those that would like to purchase credits. Each market has unique obligations and requirements. Farmers and those advising farmers are confused about the marketplace. The purpose of this document is to provide useful information about the voluntary and compliance markets. The target audience is farmers, crop consultants, and scientists. The document is organized into four sections, general information about the markets, answers to questions from certified crop consultants, market glossary, and requirements about specific markets. These marketplaces are rapidly evolving and will likely change as the markets mature.
Around the world, water is considered a fundamental factor, and plays a role in public health and economic growth. Both the water development rates and the proportion of the population are directly related to water needs. Water quality regarding physiochemistry and microbiology is important in dietary needs. Drinking water is considered one of the most important food products. Therefore, the water should meet the recommended quality standards. So, it should be free of bacteria, parasites, all kinds of microorganisms, and chemical substances which are dangerous to human health. This research focused on five cities of the Alkalaa Municipal Community, which forms 43% of the inhabitants of this community, in the Bint Jbeil district south of Lebanon. The goal of this study is to determine the fundamental physicochemical and microbiological water properties of eight distinct sites, as well as the amount of pollution. These tests were carried out in accordance with World Health Organization criteria (WHO). The collected data were utilized to assess the level of pollution in the examined zone.
Malting barley productivity and grain quality are of critical importance to the malting and brewing industry. In this study, we analyzed two experiments: a multi-environment variety trial and a nitrogen management trial. In the first experiment, we analyzed 12 malting barley genotypes across eight locations in California and three years (2017-18, 2018-19 and 2020-21). The effects of genotype (G), location (L), year (Y) and their interactions were assessed on grain yield (kg ha-1), grain protein content (GPC; %), individual-grain weight, grain size (plump and thin; %), onset gelatinization temperature (GT), peak GT, offset GT, difference between onset and peak GT and difference between peak and offset GT. L, Y and their interaction explained the largest variance for all traits except peak GT and difference between onset and peak GT, for which G explained the largest variance. The 2020-21 samples formed partially distinct clusters in principal component analysis, mainly discriminated by high percentage of thin grains and high onset GT. In the second experiment, we analyzed a dataset with two genotypes across three locations (with varying nitrogen fertilizer levels) from the 2016-17 season to assess the effect of added nitrogen on the same traits. Added nitrogen at tillering explained 18% of variance in the difference between onset and peak GT, and 5% of the variance in GPC, but was minimal for all other traits, with the largest variance explained by location and genotype. These findings illustrate the key roles of G, L and Y in determining malting barley productivity and quality.
Covid- 19 dominantly impacted the Indian agricultural sector. During the period of COVID-19 the southwest monsoon covered a major part of the country, thus resulting in an increase of 9 percent coverage in rainfall than the usual average period. Due to the good amount of rainfall the area under cultivation during the kharif season stood above 4.8% than the previous year. During, the initial lockdown period the agriculture has not been much affected and an increase in migration resulted an increase in people employed in agriculture. Through regression analysis the relationship between the yield and rainfall has been determined. The R2 values have been calculated and the spatial relationship between them has been established. Regions with higher R2 values have been found to be more dominantly affected by Covid-19, though in certain areas strong R2 has shown a weaker spatial relationship owing to certain other factors and policies taken by the Government. Therefore, regression analysis can be used as a suitable method to study the relationship of rainfall and agricultural yield during Covid-19. Keywords: Agriculture, Regression Analysis, Spatial relationship, Rainfall, Covid-19.
A common viewpoint across the Earth science community is that global soil moisture estimates from satellite L-band (1.4 GHz) measurements represent moisture only in the shallow soil layers (0-5 cm) and are of limited value for studying global terrestrial ecosystems because plants use water from deeper rootzones. Here, we argue that such a viewpoint is flawed for two reasons. First, microwave soil emission theory and statistical considerations of vertically correlated soil moisture information together indicate that L-band measurements are typically representative of soil moisture within at least the top 15-25 cm, or 3-5 times deeper than commonly thought. Second, in reviewing isotopic tracer field studies of plant water uptake, we find a global prevalence of vegetation that primarily draws moisture from these upper soil layers. This is especially true for grasslands and croplands covering more than a third of global vegetated surfaces. While shrub and tree species tend to draw deeper soil moisture, these plants often still preferentially or seasonally draw water from the upper soil layers. Therefore, L-band satellite soil moisture estimates are more relevant to global vegetation water uptake than commonly appreciated, and we encourage their application across terrestrial hydrosphere and biosphere studies.
Improving nutrient and water uptake in crops is one of the major challenges to sustain a fast-growing population that faces increasingly nutrient-limited soils. Root hairs, which are specialized epidermal cells, are important drivers of nutrient and water uptake from the soil. Microscopy provides a mean to record root hairs as digital images. However, due to their geometry and complex spatial arrangements quantifying root hairs in microscopy images manually remains a bottleneck. Manual selection of representative root hairs can result in inaccurate estimations of root hair traits and misrepresentation of root hair functions. We present a method to quantify phenotypes automatically by measuring all individual root hairs in digital microscopy images. Our method uses random forests classification to separate root hair from the parent root and the image background. We define metrics to evaluate segments of root hairs that intersect or form blobs of two or more root hairs. Using simulated annealing for combinatorial optimization, we reconstruct individual root hairs by resolving intersections in a globally optimal way. As a result, we measure the root hair length, its distribution, and root hair density in each image. We demonstrate our method on examples of three maize cultivars under phosphorus, nitrogen, and potassium stress. Results show that our measurements of root hair traits strongly correlate with manually measured data in mean root hair length (R 2 : 0.72 to 0.85, p<.001), as well as in root hair density (R 2 : 0.38 to 0.66, p<.001). We show that our method computes reliable estimates of root hair length, density and their distributions along the root on complex root hair arrangements in maize. We believe that our study paves a way towards identifying the genetic control of root hair traits and increased agricultural production.
Crop pest detection and mitigation remains an extremely challenging task for the farmers. Majority of the pest classification and detection techniques rely on supervised deep learning frameworks that require significant human intervention in labeling the input data, thereby making the down-stream tasks tedious. Therefore, this study presents a self-supervised learning (SSL) approach to classifying 12 types of agricultural insect pests from 9549 RGB images, by leveraging the Bootstrap your own latent (BYOL) algorithm. SSL uses minimal labeling and is indifferent to data augmentations or distortions. Hence, latent representations from pretrained SSL networks could be generalized well for downstream tasks like classification or object detection. For desirable classification of the insect images, the greatest challenges observed were: i) large intra-class variation (the same insect was found with different colors and patterns), and ii) complex background with inconspicuous foreground. Hence, to overcome these issues and aid generalizability of the representations learned through BYOL, entropy-guided segmentation (segments based on texture not color), is proposed as input to the SSL network in this study. Both raw and segmented images were separately fed to two independent BYOL SSL networks, i.e., with ResNet18 and ResNet50 architectures as the backbone. The efficacy of the latent representations for downstream applications was assessed using linear evaluation, and subsequently compared with traditional transfer learning outcomes from ResNet18 and ResNet50. The results indicated that, while ResNet50 backbone intuitively performed better in all cases, SSL aided with entropy-based segmentation led to ~94% classification accuracy compared to raw images (with ~90% maximum accuracy).