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Mapping irrigation types in the northwestern US using deep learning classification
Full text linkMany agricultural areas of the western United States practice irrigation using a variety of irrigation methods. Maps of irrigation methods are needed but no technology exist to produce these maps at broad spatial scales. In this study, we develop an irrigation methods mapping tool by training a U-Net model on Landsat 5- and 8-derived input images. Training data consisted in irrigation methods classified as Flood (F), Sprinkler (S) or Other (O) on agricultural fields from the Utah Water Related Land Use (WRLU) dataset and additional labeling in selected areas of southern Idaho. An ensemble of 10 trained models had an overall accuracy of 0.78. Precision for F, S and O were 0.73, 0.82 and 0.80 while recall values were 0.75, 0.74 and 0.84 respectively. Model performance was generally stable throughout the training years but varied by areas. The best performance was obtained in regions with uniform irrigation method across large patches while small fields of contrasting irrigation method with their surroundings were inadequately predicted. Model prediction of sprinkler irrigation in an irrigated watershed of southern Idaho for 2006, 2011, 2013, and 2016 were consistent with previously published survey data. Performance improvements are expected with the utilization of higher resolution satellite products. This methodology provides a tool for water resource managers to estimate irrigation methods in large agricultural areas and identify priority areas in need of irrigation methods conversion
Whole genome sequencing of Leuconostoc suionicum and L. pseudomesenteroides isolates extracted from sugar beet roots
Full text linkLeuconostoc suionicum and Leuconostoc pseudomesenteroides are important lactic acid bacteria identified in rotted tissues of roots in the field and stored sugar beets. Here, we announce the genomes of L. suionicum and L. pseudomesenteroides, isolated from post-harvest sugar beet roots from Idaho and Minnesota
Establishing a standard protocol for soil texture analysis using the laser diffraction technique
Full text linkOptical methods including laser diffraction have been increasingly used to measure soil texture and particle size distribution. However, they have not been adopted yet as a routine methodology mainly due to the difficulties in comparing their results to more commonly-used techniques (i.e., sedimentation methods). Many attempts exist in the literature to find an agreement between methodologies with relative success. In this work, we aim to improve the agreement between methodologies by adjusting parameters of the laser diffraction analysis, including sample treatment (chemical dispersion, carbonate removal, sand separation), mode of sample addition (sub-sampling versus transmittance matching), and analysis parameters (time of sonication, refractive index). Soil texture class determined by laser diffraction agreed with the sieve-hydrometer method in 98% of the runs when the following parameters were used: (1) Refractive index of 1.44 - 0.100i, (2) 180 seconds of sonication, (3) sand sieving prior to analysis, and (4) sample dispersion by shaking the sample for 1 hour with 5% of sodium hexametaphosphate. We observed that adding the entire sample to the analyzer (1 g of soil in 100 mL of dispersant) while keeping the appropriate levels of transmittance through dilution (transmittance matching) is a better way of sample addition in comparison to sub-sampling, especially for coarser soil samples. This work proposes a standard operation procedure that may broaden the adoption of laser diffraction analysis as a routine soil texture methodology
Validation of NH3 observations from AIRS and CrIS against measurements from DISCOVER-AQ and the Magic Valley
Full text linkAmmonia is one of the most common forms of reactive nitrogen and the primary alkaline gas in the atmosphere. Intended and unintended releases of ammonia into the environment over the last century have significantly altered the natural nitrogen cycle, so that the current emission levels of ammonia are about four times higher than in previous centuries. Ammonia is the dominant base in the atmosphere, and it plays a significant role in the formation of fine particulate matter (PM2.5) which can penetrate deep into the lungs and severely impact the respiratory and circulatory systems. In situ measurement of ammonia remains a challenge as ammonia is easy to detect, but it is hard to measure accurately. The high spatial and temporal variability of ammonia exacerbates the lack of continuous, spatially well sampled data over extensive regions. Satellite data, even though they come with their own uncertainties, provide by virtue of their spatial and temporal density, another option for quantifying ammonia emissions. Our objective was to add to the satellite validation record at the single pixel scale, using aircraft and ground ammonia measurements with satellite retrievals from both the Atmospheric Infrared Sounder (AIRS) and Cross-track Infrared Sounder (CrIS) instruments. The AIRS and CrIS profiles individually have large uncertainties, which are driven by local conditions, most significantly temperature profiles and sub-pixel heterogeneity. However, average biases between satellite and aircraft data, after smoothing errors are accounted for, are below or close to 1 ppbv. Use of ground base measurements for validation clearly demonstrate the importance of having more than a few dozen data points to obtain useful information from space-based retrievals of ammonia. With 464 observations over three years, over a small region, it was possible to obtain a clear picture of the source distribution in the region through the application of a physics based oversampling algorithm
Safe and sustainable use of bio-based fertilizers in agricultural production systems
Full text linkRecycling and/or upcycling of agricultural byproducts containing valuable nutrients back into agricultural systems as bio-based fertilizers can improve the circularity and sustainability of food production. However, in some instances, there may be negative environmental consequences or safety concerns that need to be carefully considered. One of the most common issues regarding the use of bio-based fertilizers, in particular livestock manure, is the over concentration of nutrients in regions with intensive production leading to losses of reactive nitrogen and phosphorus into the environment. This can have negative impacts on air and water quality as well as contribute to climate change. In addition, some bio-based fertilizers may contain heavy metals, pathogens, antibiotics, and other contaminants that can pose a health risk to humans, animals, crops and the ecosystem. Recognizing and managing these risks is necessary to fully integrate these products back into production systems, thereby enhancing the circularity of agriculture
Evaluation of canopy temperature based crop water stress index for deficit irrigation management of sugaar beet in semi-arid climate
Full text linkSugar beet is an economically important crop in the semi-arid Intermountain Western U.S with seasonal water use ranging from 500 to 900 mm. Sugar beet is a deep-rooted crop in unrestricted soil profiles that can readily utilize stored soil water to reduce seasonal irrigation requirements. Effective use of stored soil water below 0.6 m requires precise irrigation scheduling and knowledge of soil water availability below 0.6 m, which is usually unavailable due to the labor and expense of soil water monitoring at deeper depths and uncertainty in effective rooting depth and soil water holding capacity. Deficit irrigation (DI) management of sugar beet using thermal-based crop water stress index (CWSI) has the potential to overcome soil water monitoring limitations and facilitate utilization of stored soil water to reduce seasonal irrigation requirements. The objective of the research summarized in this paper was to implement and evaluate the effect of automated DI scheduling of sugar beet, using three daily average CWSI thresholds (0.2, 0.35 and 0.55) on seasonal irrigation requirement, crop evapotranspiration, seasonal soil water depletion, root yield, estimated recoverable sugar (ERS) yield and water use efficiency compared to full irrigation. There were no significant differences in root and ERS yield between full irrigation and 0.2 CWSI DI treatment while seasonal ET was significantly decreased, seasonal soil water extraction was significantly increased, and seasonal irrigation depths were reduced 133 to 185 mm. Root and ERS yield water production functions were curvilinear with a downward concave. Root and ERS yield water use efficiencies were constant or increased slightly for crop evapotranspiration reductions up 85% of full irrigation evapotranspiration. The results indicate that irrigating when average daily CWSI sugar beet exceeds 0.2 is an effective means for mild deficit irrigation scheduling to reduce seasonal irrigation requirements with no significant effect on root and ERS yield
Effect of dairy manure-based fertilizers on nitrous oxide emissions in a semi-arid climate
Full text linkInterest in manure treatment technologies that address challenges associated with nutrient management while minimizing environmental impacts, continues to grow. However, more research is needed to understand how manure treatment byproducts impact nutrient availability and greenhouse gas emissions (GHG). This study investigated the impact of two manure-based fertilizer sources (Dissolved Air Flotation [D] and Mechanical Vapor Recompression Solids [VR]) on N2O and CO2 emissions, soil nutrients, and crop yields in a forage rotation. The study consists of three treatments applied in plots with [M] or without [NM] previous manure application under a continuous corn (Zea mays) and triticale (x Triticosecale) rotation. Soil samples were collected, and forage yield was measured annually. Gas fluxes were measured throughout the year to determine daily and cumulative emissions. On average, M had significantly greater SOC, TC, TN, (30-37%), and reduced soil NH4-N (15%). Corn yield was 7% lower on M vs NM plots in 2021 and 17% greater on VRNM vs CNM plots in 2022. Triticale yield was 17% greater on M vs NM plots in 2021 and greatest on VR and DM plots compared to remaining NM plots in 2022. In general CO2-C and N2O emissions were greatest on M plots (27-90% and 9-287%, respectively) in 2021 and on D and VR plots in 2022 (27-70% and 434-799%, respectively). Over both years, VR lost 1.9-2.2% of N applied as N2O-N while D lost 0.4-0.8%. In semi-arid systems, DAF solids may provide a successful alternative to help reduce GHG emissions without compromising crop yield
Malt Barley Yield and Quality Response to Crop Water Stress Index
Full text linkApplication of canopy temperature-based crop water stress index (CWSI) for monitoring plant water stress and scheduling irrigation requires reliable estimation of well-watered (TLL) and non-transpiring (TUL) canopy temperatures under identical climatic conditions. A 3-year field study was conducted to develop and evaluate the use of data driven models to estimate TLL and TUL of irrigated spring malt barley. Five irrigation rates with four replicates each were used: full irrigation (FIT), 75, 50 and 25% of FIT and no irrigation. Three replicate continuous canopy temperatures measurements were taken in each irrigation treatment starting the first week in June ending in mid-July along with meteorological conditions. A feed forward neural network (NN) model was used to predict TLL between 13:00 and 15:00 MDT based on model inputs: solar radiation, air temperature, relative humidity, and wind speed for the same period. A physical model calibrated to the data set was used to estimate TUL. The NN model predicted TLL was well correlated with measured TLL (R2 = 0.99) with root mean square error 0.89°C and mean absolute error 0.70°C. There were significant differences in calculated daily average CWSI between irrigation treatments. Relative evapotranspiration, relative malt barley seed yield, seed test weight and percent plump kernels were negatively correlated with season average CWSI. Malt barley seed protein was positively correlated with season average CWSI. The relationship between daily average CWSI and fraction available soil water was well described by a two-parameter exponential decay function (R2 = 0.72). These results indicate applicability of data driven models for computing CWSI of irrigated spring malt barley in a semi-arid environment and demonstrate malt barley yield response to crop water stress
Predicting nitrogen mineralization from long term application of dairy manure in a semiarid cropping system
Full text linkApproximately 37% of US milk production occurs in semiarid regions providing an opportunity to recycle manure nutrients through a variety of cropping systems. Accurate prediction of nitrogen (N) mineralization is critical to determine manure application suitability in intensive irrigated agriculture as many crops in the region have quality parameters that are sensitive to N. Research was conducted in south central Idaho to evaluate N mineralization via a buried bag methodology to develop a predictive N-mineralization model. The study was arranged in a randomized complete block design with manure application rates of 18, 36, and 52 Mg ha-1 both annually and biennially with synthetic fertilizer and untreated check treatments. The crop rotation included small-grain and broadleaf crops. Greater manure applications resulted in an increase in soil organic matter (SOM), total nitrogen (TN), and nitrate (NO3-N) at the culmination of the study. Nearly five-times as much N was mineralized annually in the 0 to 30 cm depth as compared to the 30 to 60 cm depth. Increased rates of mineralization for each kg of added N occurred in years when residue from broadleaf crops (slope = 0.17) was applied as compared to small-grain years (slope = 0.07). Stepwise modeling determined that the most predictive model for seasonal N mineralization (R2 = 0.79) included manure N, residue N, soil organic matter, and electrical conductivity. These results allow preplant N mineralization estimation and will prove critical for managing manure in semiarid regions for agronomic, economic, and environmentally sound crop production
Spatial distribution of ammonia concentrations and modelled dry deposition in an intensive dairy production region
Full text linkAgriculture generates ~83% of total U.S. ammonia (NH3) emissions, potentially adversely impacting sensitive ecosystems through wet and dry deposition. Regions with intense livestock production, such as the dairy region of south-central Idaho, generate hotspots of NH3 emissions. Our objective was to measure the spatial and temporal variability of NH3 across this region and estimate its dry deposition. Ambient NH3 was measured using diffusive passive samplers at 8 sites in two transects across the region from 2018-2020. NH3 fluxes were estimated using the Surface Tiled Aerosol and Gaseous Exchange (STAGE) model. Peak NH3 concentrations were 4-5 times greater at a high-density dairy site compared to mixed agriculture/dairy or agricultural sites, and 26 times greater than non-agricultural sites with prominent seasonal trends driven by temperature. Annual estimated dry deposition rates in areas of intensive dairy production can approach 50 kg Nitrogen ha/yr, compared to < 1 kg Nitrogen ha/yr in natural landscapes. Modeling work highlighted a need for better understanding of soil emission potential in environments with high soil pH and low leaf area. Research toward better understanding soil processes is needed to improve understanding of ammonia dry deposition to arid and sparsely vegetated natural ecosystems across the western U.S