Simulating the Soil Erosion from Land Removed from CRP

A survey of South Dakota Conservation Reserve Program (CRP) contract holders, conducted in 2007, indicated that large areas of land in the CRP could be returned to grain production in the next four years. Conversion of these acres from CRP back to row crop production without regard to environmental quality could have huge impacts on water quality in the state. The objective of this study was to calibrate an erosion prediction model and use it to estimate increased soil erosion due to this land use change. The Water Erosion Prediction Project (WEPP), a process based erosion prediction model developed by USDA, was utilized in this study for this purpose. Runoff and sediment yield data from a continuous com field in Brookings, South Dakota were used to calibrate the model. Data collection took place during frost free season of 2009. To determine hillslope and channel networks in the watershed GeoWEPP and ArcMap were utilized. Input DEM and ASCII files were generated from GPS data of watershed and soil map was obtained from Geospatial Datagateway, NRCS, USDA website. A WEPP management file for continuous com was adjusted to simulate actual field conditions. Initial soil moisture values were also adjusted to simulate actual field conditions at the time of each runoff event for single storm simulation. Initially, for single storm simulation, WEPP predicted the sediment yield quite satisfactory but it heavily under predicted the runoff. To evaluate the model performance Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE) -observation standard deviation ratio (RSR) and percent bias (PBIAS) were calculated. For runoff prediction WEPP performance was considered unsatisfactory (NSE =-0.32 and RSR =1.15) and for sediment yield prediction WEPP performance was considered very good (NSE =0.95 and RSR = 0.23). Because of very good sediment yield prediction, WEPP calibration was not performed. This uncalibrated model was then used to predict annual soil loss for different management practices including CRP across four different locations in South Dakota. Unalibrated WEPP indicated that when changing the land use from CRP to other management annual soil erosion was increased by various amounts depending upon the management practice, watershed slope, soil type and location. The least increase was indicated when adopting no-till option and highest increase was indicated for soybean spring chisel and fall MB plow. For Brookings watershed, having existing soils and slope, sediment yield increased by 4.9 to 5.5 tons/ha per year while switching the land use from CRP to continuous com spring chisel plow or com-soybean spring chisel plow. Also if the slope is higher than 4% at Brookings location, for- existing soil and slope conditions, these two management practices have soil loss higher than soil loss tolerance limit (T ~ 11 ton/ha), indicating unsustainable land use. For Brookings site least increase of 0.4 to 0.9 ton/ha per year was indicated for no till practices (Com no till and com soybean no till)

Applications of Remote Sensing in Precision Agriculture: A Review

Agriculture provides for the most basic needs of humankind: food and fiber. The introduction of new farming techniques in the past century (e.g., during the Green Revolution) has helped agriculture keep pace with growing demands for food and other agricultural products. However, further increases in food demand, a growing population, and rising income levels are likely to put additional strain on natural resources. With growing recognition of the negative impacts of agriculture on the environment, new techniques and approaches should be able to meet future food demands while maintaining or reducing the environmental footprint of agriculture. Emerging technologies, such as geospatial technologies, Internet of Things (IoT), Big Data analysis, and artificial intelligence (AI), could be utilized to make informed management decisions aimed to increase crop production. Precision agriculture (PA) entails the application of a suite of such technologies to optimize agricultural inputs to increase agricultural production and reduce input losses. Use of remote sensing technologies for PA has increased rapidly during the past few decades. The unprecedented availability of high resolution (spatial, spectral and temporal) satellite images has promoted the use of remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient application, disease and pest management, and yield prediction. In this paper, we provide an overview of remote sensing systems, techniques, and vegetation indices along with their recent (2015–2020) applications in PA. Remote-sensing-based PA technologies such as variable fertilizer rate application technology in Green Seeker and Crop Circle have already been incorporated in commercial agriculture. Use of unmanned aerial vehicles (UAVs) has increased tremendously during the last decade due to their cost-effectiveness and flexibility in obtaining the high-resolution (cm-scale) images needed for PA applications. At the same time, the availability of a large amount of satellite data has prompted researchers to explore advanced data storage and processing techniques such as cloud computing and machine learning. Given the complexity of image processing and the amount of technical knowledge and expertise needed, it is critical to explore and develop a simple yet reliable workflow for the real-time application of remote sensing in PA. Development of accurate yet easy to use, user-friendly systems is likely to result in broader adoption of remote sensing technologies in commercial and non-commercial PA applications

Applications of remote sensing in precision agriculture: A review

Agriculture provides for the most basic needs of humankind: food and fiber. The introduction of new farming techniques in the past century (e.g., during the Green Revolution) has helped agriculture keep pace with growing demands for food and other agricultural products. However, further increases in food demand, a growing population, and rising income levels are likely to put additional strain on natural resources. With growing recognition of the negative impacts of agriculture on the environment, new techniques and approaches should be able to meet future food demands while maintaining or reducing the environmental footprint of agriculture. Emerging technologies, such as geospatial technologies, Internet of Things (IoT), Big Data analysis, and artificial intelligence (AI), could be utilized to make informed management decisions aimed to increase crop production. Precision agriculture (PA) entails the application of a suite of such technologies to optimize agricultural inputs to increase agricultural production and reduce input losses. Use of remote sensing technologies for PA has increased rapidly during the past few decades. The unprecedented availability of high resolution (spatial, spectral and temporal) satellite images has promoted the use of remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient application, disease and pest management, and yield prediction. In this paper, we provide an overview of remote sensing systems, techniques, and vegetation indices along with their recent (2015–2020) applications in PA. Remote-sensing-based PA technologies such as variable fertilizer rate application technology in Green Seeker and Crop Circle have already been incorporated in commercial agriculture. Use of unmanned aerial vehicles (UAVs) has increased tremendously during the last decade due to their cost-effectiveness and flexibility in obtaining the high-resolution (cm-scale) images needed for PA applications. At the same time, the availability of a large amount of satellite data has prompted researchers to explore advanced data storage and processing techniques such as cloud computing and machine learning. Given the complexity of image processing and the amount of technical knowledge and expertise needed, it is critical to explore and develop a simple yet reliable workflow for the real-time application of remote sensing in PA. Development of accurate yet easy to use, user-friendly systems is likely to result in broader adoption of remote sensing technologies in commercial and non-commercial PA applications

Evaluation of Gridded Precipitation Data for Hydrologic Modeling in North-Central Texas

Over the past few decades, several high-resolution gridded precipitation products have been developed using multiple data sources and techniques, including measured precipitation, numerical modeling, and remote sensing. Each has its own sets of uncertainties and limitations. Therefore, evaluating these datasets is critical in assessing their applicability in various climatic regions. We used ten precipitation datasets, including measured (in situ), gauge-based, and satellite-based products, to assess their relevance for hydrologic modeling at the Bosque River Basin in North-Central Texas. Evaluated datasets include: (1) in situ station data from the Global Historical Climate Network (GHCN); (2) gauge-based dataset Daymet and the Parameter-elevation Regression on Independent Slope Model (PRISM); (3) satellite-based dataset Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Early and Late, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and PERSIANN-CCS (Cloud Classification System); (4) satellite-based gauge-corrected dataset IMERG-Final, PERSIANN-CDR (Climate Data Record), and CHIRPS (Climate Hazards Group Infrared Precipitation with Station data). Daily precipitation data (2000–2019) were used in the Soil and Water Assessment Tool (SWAT) for hydrologic simulations. Each precipitation dataset was used with measured monthly United States Geological Survey (USGS) streamflow data at three locations in the watershed for model calibration and validation. The SUFI-2 (Sequential Uncertainty Fitting) method on the SWAT-CUP (Calibration and Uncertainty Program) was used to quantify and compare the uncertainty in streamflow simulations from all precipitation datasets. The study has also analyzed the uncertainties in SWAT model parameter values under different gridded precipitation datasets. The results showed similar or better model calibration/validation statistics from gauge-based (Daymet and PRISM) and satellite-based gauge-corrected products (CHIRPS) compared with the GHCN data. However, satellite-based precipitation products such as PERSIANN-CCS and PERSIANN-CDR unveil comparatively inferior to capture in situ precipitation and simulate streamflow. The results showed that gauge-based datasets had comparable and even superior performances in some metrics compared with the GHCN data

Improved Water and Economic Sustainability with Low-Input Compact Bed Plasticulture and Precision Irrigation

Raised-bed plasticulture is used globally to produce fresh market vegetables, fruits, and other crops. A novel compact bed geometry, designed to improve the system efficiency of plasticulture, was evaluated for its ability to reduce water and other inputs and facilitate the adoption of precision irrigation using savings from reduced input costs. Using measurements and modeling (HYDRUS), precision soil moisture-based irrigation management (SM) and grower-based irrigation management (GR) were evaluated for conventional and compact beds. Similar to previous studies, SM reduced applied irrigation by 20% and deep percolation losses by 30% compared to GR for conventional beds. However, the significant investment needed to buy and maintain modern soil moisture measurement systems is likely to limit the adoption of SM. Compact bed geometries, taller and narrower than conventional beds, can sustain yields while reducing inputs of water, pesticide, fertilizer, fuel, and plastic. Cost savings of $154-$789/ha associated with the reduced inputs for compact beds can cover the cost of a soil-moisture sensor network with an automated irrigation system for a typical 65-ha fresh produce farm. Model simulations showed that compact beds with SM reduced irrigation volume by 8%-36% and deep percolation losses by 18%-54% compared to the traditional practice of conventional beds with GR. Compact beds with SM also reduced runoff volume by increasing evaporation by 15%-35%, available soil water storage by 12%-13%, and field infiltration of rainfall by 9%-18%. A basin-wide adoption of compact beds with precision irrigation can help reduce excessive downstream flows and nutrient loads in warmer and ecologically sensitive production regions such as the Everglades and the Chesapeake Bay

Bi-decadal groundwater level trends in a semi-arid south indian region: Declines, causes and management

Study region: Three districts in crystalline aquifer region of semi-arid south India. Study focus: India, world’s largest groundwater user (250 billion m3 yr−1) has been reported to experience declining groundwater levels. However, the statistical significance of the decline has not been analyzed to separate human effects from natural variability. Trends in groundwater levels in three administrative districts of south India were analyzed and explained through changes in irrigation, rainfall, and agricultural power subsidy. New hydrological insights for the region: Contrary to common perception of widespread groundwater declines only 22–36% of the wells showed statistically significant declines. The use of well depth during dry well periods may slightly underestimate the number of declining wells (by 1%) and rate of decline. Increase in groundwater irrigated area combined with rainfall and power subsidy policy, were the main causative factors for the decline. Groundwater decline after implementation of free-electricity policy in 2004 confirmed the nexus between power subsidy and groundwater. These declines are likely to worsen due to future well drillings. Trends in other regions with similar hydro-geologic conditions need to be analyzed to verify groundwater declines and its linkages with power subsidy. Once established, reforms in power subsidy and well permit policy along with conversion to efficient micro–irrigation may be needed to maintain or enhance groundwater availability in the crystalline aquifer region of India (240 million ha)