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.
Evaluating the influence of grass or broadleaf cover crops on soil health measurements is common in the U.S Midwest. However, the comparison among different cover crop mixtures, including blends of both grass and broadleaf species is limited. Eleven cover crop experiments were conducted in South Dakota from 2018-2020. Cover crops were planted in the fall after small grains harvest as mixtures of dominantly grasses or broadleaves, a 50/50 grass/broadleaf mixture, and a no cover crop control. Soil and plant surface residue samples were collected in the fall before winter kill and in the spring before cover crop termination and corn planting. Soil samples were analyzed for permanganate oxidizable carbon (POXC), potentially mineralizable nitrogen (PMN), and soil respiration. Cover crops regardless of composition compared to the no cover crop control did not affect fall or spring cover crop/previous crop residue biomass in 7 of the 11 site-years, suggesting growing cover crops may accelerate decomposition of previous crop residue. Cover crops did not improve soil health measurements compared to the no cover crop control or were there differences among cover crop mixtures. Weather and soil properties (precipitation, soil organic matter, and pH) were related to differences in soil heath measurements among site-years. In the first year of planting a multi-species mixture of grasses and/or broadleaves after small grain harvest, growers should not expect to find differences in soil health measurements. Long-term trials are needed to determine whether these different cover crop mixtures over time result in changes in soil health.
Accurate estimates of the soil water balance components are critical for optimizing irrigation water use in agricultural fields. Estimates are normally obtained using simple water balance models and for representative areas, not taking into consideration the within variability of soil properties. In this study, we used the MOHID-Land distributed process-based model to compute the variability of the soil water balance components in a 23ha almond field located in southern Portugal, at a resolution of 5m. The main objective was the possible assessment of management zones for improving water productivity in that water-scarce region. An electromagnetic induction survey was carried out first to obtain electromagnetic conductivity images which provided the spatial distribution of the real soil electrical conductivity (ff) with depth. The spatial distribution of ff was then correlated to soil particle size distribution using an in-situ calibration. Afterward, pedotransfer functions were applied to define the soil hydraulic parameters necessary to run the distributed model and map the within soil variability at the field scale. Irrigation data was monitored on-site, at two locations, while weather data was extracted from a local meteorological station. The distributed modeling approach included the definition of potential evapotranspiration fluxes computed from the product of the reference evapotranspiration obtained according to the FAO56 Penman-Monteith equation and a crop coefficient for each stage of almond’s growing season, the variable-saturated flow using the Richards equation, and root zone water stress following a macroscopic approach. Modeling results were used to present the maps of the variability of the seasonal actual crop transpiration and soil evaporation, the mean soil moisture, seasonal runoff, and seasonal percolation. Then, management zones for improving irrigation water use in the studied almond field were proposed.
Plant roots are responsible for essential functions like nutrient uptake, anchorage, and storage. Study of root uptake mechanisms for macro nutrients like nitrogen, phosphorus, potassium, and sulphur is vital to our understanding of their role in plant growth and development. Small signaling peptides (SSPs), are hormones which regulate diverse plant developmental processes including root growth. However, their involvement in regulation of nutrient uptake by roots is poorly understood. We recently developed a hydroponics- based plant growth system which combines ion chromatography with synthetic peptide application, to analyze the depletion rates of nutrients by Medicago truncatula roots. Application of the synthetic SSP MtCEP1 and AtCEP1 led to enhanced uptake of nitrates, sulphates, and phosphates. To further elucidate the molecular mechanism of nutrient uptake mediated by these peptides, we conducted an RNAseq of M. truncatula roots treated with the peptides. A differential gene expression analysis revealed thousands of peptide responsive genes. Several known nitrate transporters and a sulphate transporter AtSULTR3:5-like gene showed enhanced expression under both, MtCEP1 and AtCEP1 peptide application. Multiple, as of yet uncharacterized, CEP peptide responsive pathway regulatory genes such as kinases and transcription factors were also identified through this transcriptomic analysis. This study highlights the potential of phenomics enabled biology to uncover target genes for improving agriculturally important traits such as nutrient uptake.
Khose Suyog Balasaheb 1, Damodhara Rao Mailapalli2, Chandranath Chatterjee31 Research Scholar, Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal2 Associate Professor, Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal3 Professor, Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal
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.
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.
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.
Understanding three-dimensional (3D) root traits is essential to improve water uptake, increase nitrogen capture, and raise carbon sequestration from the atmosphere. However, quantifying 3D root traits by reconstructing 3D root models for deeper field-grown roots remains a challenge due to the unknown tradeoff between 3D root-model quality and 3D root-trait accuracy. Therefore, we performed two computational experiments. We first compared the 3D model quality generated by five state-of-the-art open-source 3D model reconstruction pipelines on 12 contrasting genotypes of field-grown maize roots. These pipelines included COLMAP, COLMAP+PMVS (Patch-based Multi-view Stereo), VisualSFM, Meshroom, and OpenMVG+MVE (Multi-View Environment). The COLMAP pipeline achieved the best performance regarding 3D model quality versus computational time and image number needed. Thus, in the second test, we compared the accuracy of 3D root-trait measurement generated by the Digital Imaging of Root Traits 3D pipeline (DIRT/3D) using COLMAP-based 3D reconstruction with our current DIRT/3D pipeline that uses a VisualSFM-based 3D reconstruction (Liu et al., 2021) on the same dataset of 12 genotypes, with 5~10 replicates per genotype. The results revealed that, 1) the average number of images needed to build a denser 3D model was reduced from 3000~3600 (DIRT/3D [VisualSFM-based 3D reconstruction]) to 300~600 (DIRT/3D [COLMAP-based 3D reconstruction]); 2) denser 3D models helped improve the accuracy of the 3D root-trait measurement; 3) reducing the number of images can help resolve data storage capacity problems. The updated DIRT/3D (COLMAP-based 3D reconstruction) pipeline enables quicker image collection without compromising the accuracy of 3D root-trait measurements.
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.
Lebanon’s natural water resources are facing serious problems and approaches exhaustion. One of these issues is deteriorating performance, which is linked to unregulated resource planning and rising demand. There are many different types of consumption, such as residential, industrial, and irrigation. Surface and groundwater are both referred to designate water resources. However, due to the obvious accessibility of exploitation, surface water resources such as rivers, lakes, and basins are primarily used. The Ras El-Ain basin is 6 km far south of Tyr, Lebanon. The Lebanese state dedicated it, along with other reservoirs, to supply potable water for Tyr and the surrounding villages. Today, these basins’ water quality has deteriorated significantly because of unrestricted liquid and soil waste dumping. As a result, contaminants develop in the basin water. Aside from laboratory testing for water quality, contamination can be seen through direct observations, odors, watercolors, and patterns. The purpose of this study is to assess the level of pollution in the Ras El-Ain basin. This basin has been progressively subjected to a variety of quality degradation characteristics. This includes the most important physiochemical properties. As a result, the physicochemical and microbiological water characteristics of five selected samples from each basin were tested. These tests were performed in accordance with European Standard Methods and World Health Organization guidelines (WHO). The effect of pollutant disposal in the Ras El-Ain basin was studied using multivariate approaches. The obtained results were used to evaluate the pollution degree in various regions of the basin.
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.
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.
Environmental justice and equity should include access to clean water for all. It is expensive to drill borehole wells, typically over $10,000 US dollars, and so organizations working to provide wells in developing countries have typically installed community wells at some common gathering place. This requires that many users must walk long distances to access these water sources. This limits the quantity of water available to a family, and also creates vulnerabilities for the family member, usually a woman or child, sent for the water since the journey is often made early in the morning or at night in the dark. I have been drilling wells with a Kenyan team since 2010 using a simple, manual percussion hydraulic method developed by WaterForAllinternational.org whereby we can install a well generally for less than $200 US dollars excluding labor. Through their own participation in the drilling process, this low-cost enables families to pay for and drill their own well. In this way, they gain access to a much larger supply of water at or close to home, and eliminate the need and vulnerability associated with walking long distances to procure water for their family. Both the drilling apparatus and the cased well, including the pump, is constructed from materials available off-the-shelf at local hardware stores. Over the years I have made several modifications to the pump design, other infrastructure, and manufacturing process to improve the longevity, simplicity, and interchangeability of the final product. The drilling method is primarily applicable to aquifers lying above bedrock and it is feasible to drill wells to a depth of several hundred feet. The greatest challenge in the endeavor is earning the trust and cultivating the participation of the local community. This presentation will address the drilling process, the well infrastructure, and some socio-cultural aspects of the project.
Agricultural producers have many incentives to clear small natural areas from their fields, as this can expand their cultivated land base. However, natural areas can play a role in delivering ecosystem services that improve crop productivity (e.g., by providing habitat for beneficial arthropods, that deliver pollination or pest control). We assessed the impact of landscape complexity on adjacent canola (Brassica napus) yield at both the field- and subfield-level using remotely sensed products. Fields with higher landscape complexity generally had higher mean yields. However, fields surrounded mostly by either crop or non-crop covers had lower yields, possibly due to a lack of ecosystem services (i.e., pollination or natural pest control services) or a strong yield-reducing edge effect. At the subfield-level, we found evidence of a boost in yield between 30 and 100 m from the field edge towards its center, as well as a potential yield-stabilizing effect at the same range.
Higher temperatures across the globe are causing an increase in the frequency and severity of droughts. In agricultural crops, this results in reduced yields, financial losses for farmers, and increased food costs at the supermarket. Root architecture plays a major role in a plant’s ability to survive and perform under drought conditions but phenotyping root growth to determine the genetic and environmental factors involved is extremely difficult due to roots being under the soil. RootBot is an automated high-throughput phenotyper that eliminates many of the difficulties and time constraints for performing multiple drought-stress studies. RootBot can simulate plant growth conditions during the first 72 hours of growth using transparent plates filled with soil (as opposed to synthetic media such as agar). RootBot has the capacity for up to 50 plates at a time, however, designing a system to organize these plates, image them at the appropriate times, and save and analyze the data for many plates simultaneously is challenging. To improve upon the pipeline, we incorporate strategies from existing phenotyping pipelines into the imaging and measurement processes. We will also investigate the genotypes using GWAS to identify sequence variants associated with drought tolerance or lack thereof. This pipeline will improve RootBot’s abilities in high-throughput phenotyping and the information gathered will be helpful towards future genetic engineering and breeding.
In an increasingly digitised and data-driven world, there is a pressing need for globally reproducible high-throughput morphological phenotyping, which provides quantitative and objective data and markers of seed quality to guide analysis and research. Current lab-based methods for morphological phenotyping of germinating seeds still largely rely on visual or 2D-imaging technologies with their respective limitations. Here we present the phenoTest, a novel high-throughput 3D-phenotyping technology that alleviates many of the drawbacks of conventional testing and research methods. Using Xray, 3D-volume image data of individual seedlings grown under highly-standardized conditions are captured. Through an AI-based algorithm, all plant organs can be automatically segmented and measured in 3D, currently outputting 50 seedling datasets per 2 minutes. Individual seedlings can be traced over time across the developmental process without disrupting the germination containers. The process can be run in fully-automated, 24h operation and is industrially validated for multiple years. The phenoTest is universally applicable with customized algorithms for all plants and crops, covering the entire range from fine grasses, vegetables to forestry seeds. The process enables a quantitative, objective and reproducible assessment of morphological seedling traits in 4D which can substitute visual gemination and vigor testing and can be harnessed to optimise processing and breeding. We will share data on the effects of a multitude of factors such as seed treatment, ageing, storage and packaging on the speed and quality of seedling development, and the homogeneity, degree of abnormalities, germination capacity and vigor of seed lots of different crop types.
Nitrogen inputs can be an important cost consideration for farmers in terms of economic profit as well as environmental impact. Elucidating genetic regions that are associated with plant phenotype response to nitrogen stress can help in facilitating breeding approaches that can mitigate these costs. A diverse population of 272 maize lines was planted at a field site in Champaign, IL in two consecutive years in reduced nitrogen conditions. 302 phenotypes were recorded including: seed ionomic content, root structural traits derived from 2-dimensional images as well as a 3-dimensional representation generated from x-ray computed tomography (XRT) scans, root traits extrapolated from mini-rhizotron systems, drone images across the growing season and end of season agronomic traits such as biomass and yield. BLUP models were fit to obtain estimates of single year genotypic values as well as across years values both of which took into account year specific spatial variation. Individual years and combined years BLUP values were used as response variables in genome wide association studies (GWAS) to identify loci significantly associated with each set of values. Significant associations were identified for all phenotype categories.