Evaluation of selected watershed characteristics to identify best management practices to reduce Nebraskan nitrate loads from Nebraska to the Mississippi/Atchafalaya River basin

Nebraskan streams contribute excess nitrogen to the Mississippi/Atchafalaya River Basin and Gulf of Mexico, which results in major water-quality impairments. Reducing the amount of nitrogen (N) exported in these streams requires the use of best management practices (BMPs) within the landscape. However, proper BMP utilization has rarely been statistically connected to potential controls of N export within watersheds, particularly precipitation and soil characteristics. In this study, 19 watershed variables were evaluated in five categories (hydrological, physiographic, point sources, land use, and soil properties) to determine the characteristics that influenced variable nitrate nitrogen (NO3-N) concentrations in 17 Nebraska watersheds with known high NO3-N export rates. Each characteristic was derived from publicly-available datasets in an effort to develop a multiregional method. Of the 19 variables evaluated, 10 variables (developed, cropland, herbaceous, forest, excessively- drained soils, precipitation, base-flow index, slope, organic matter and point sources) were identified to statistically influence stream NO3-N concentrations. The 17 watersheds were divided into five subset groups using principal component analysis. Distributions of the 10 watershed variables were then used to determine the most applicable BMPs for NO3-N reductions for each stream subset: excessively drained with high baseflow index (Groups 1 and 2), dominantly row crop land usage with well-drained soils, higher precipitation, and an increased tendency for surface runoff concerns (Group 3), highly developed watersheds (Group 4), and single river dominated by wastewater treatment plant discharge (Group 5). Based on the most influential variables a variety of BMPs were recommended, including N fertilizer application management and accounting for N credit from mineralization and NO3-N in irrigation water (Groups 1 and 2), installation of riparian buffers and wetlands (Group 3), urban BMPs such as bioretention cells and permeable pavement (Group 4), and upgrades to the wastewater treatment plant (Group 5). This study provides an improved technique for facilitating watershed management by linking BMPs directly to the characteristics of each watershed to reduce current nitrate export

Engaging Farmers and the Agriculture Industry Through the Testing Agricultural Performance Solutions Program

The University of Nebraska–Lincoln Testing Agricultural Performance Solutions (TAPS) program involves use of farm management competitions to increase engagement across producers, industry, and universities. Participants make several management decisions throughout the growing season in a controlled field trial held at the university research station. Results are analyzed, and awards are presented for most profitable farm, most efficient farm, and farm with the greatest grain yield. The TAPS program involves several techniques for facilitating participatory assistance, including two-way communication and transformational learning. It has resulted in participants\u27 questioning their past management decisions and realizing that they need to improve their marketing skills to improve profitability

Actual evapotranspiration and crop coefficients of irrigated lowland rice (Oryza sativa L.) under semiarid climate

Lowland irrigated rice is the predominant crop produced in the Senegal River Valley characterized by very low annual rainfall, high temperatures, and low relative humidity. The Senegal River is shared by Senegal, Mali, Mauritania, and Guinea, and serves as the main source of irrigation water for the adopted double rice cropping system. Developing appropriate resource management strategies might be the key factor for the sustainability of rice production in the region. This study aims to estimate rice seasonal evapotranspiration (ETa), irrigation water requirement, and to develop rice growth stage specific crop coefficients (Kc) to improve rice water productivity. Field experiments were conducted during the hot and dry seasons in 2014 and 2015 at the AfricaRice research station at Fanaye in Senegal. Irrigation water inputs were monitored and actual crop evapotranspiration was derived using the water balance method. Daily reference evapotranspiration (ETo) was estimated using the Penman-Monteith equation and the weather variables were collected at the site by an automated weather station. The results showed that the ETo during the hot and dry season from February 15th to June 30th varied from 4.5 to 9.9 mm and from 3.7 to 10.8 mm in 2014 and 2015, respectively, and averaged 6.8 mm d-1 in 2014 and 6.6 mm d-1 in 2015. The seasonal irrigation water amount for the transplanted rice was 1110 mm in 2014 and 1095 mm in 2015. Rice daily ETa varied from 4.7 to 10.5 mm in 2014 and from 4.4 to 10.5 mm in 2015 and averaged 8.17 mm in 2014 and 8.14 mm in 2015. Rice seasonal ETa was 841.5 mm in 2014 and 855.4 mm in 2015. The derived rice Kc values varied from 0.77 to 1.51 in 2014 and 0.85 to 1.50 in 2015. Rice Kc values averaged 1.01, 1.31, and 1.12 for the crop development, mid-season and late season growth stages, respectively. The Kc values developed in this study could be used for water management under rice production during the hot and dry season in the Senegal River Valley

Development an edge-computing sensing unit for continuous measurement of canopy cover percentage of dry edible beans

Canopy cover (CC) is an important indicator for crop development. Currently, CC can be estimated indirectly by measuring leaf area index (LAI), using commercially available hand-held meters. However, it does not capture the dynamics of CC. Continuous CC monitoring is essential for dry edible beans production since it can affect crop water use, weed, and disease control. It also helps growers to closely monitor “yellowness”, or senescence of dry beans to decide proper irrigation cutoff to allow the crop to dry down for harvest. The goal of this study was to develop a device – CanopyCAM, containing software and hardware that can monitor dry bean CC continuously. CanopyCAM utilized an in-house developed image-based algorithm, edge-computing, and Internet of Things (IoT) telemetry to transmit and report CC in real-time. In the 2021 growing season, six CanopyCAMs were developed with three installed in fully irrigated dry edible beans research plots and three installed at commercial farms. CC measurements were recorded at 15 min interval from 7:00 am to 7:00 pm each day. Initially, the overall trend of CC development increased over time but there were many fluctuations in daily readings due to lighting conditions which caused some overexposed images. A simple filtering algorithm was developed to remove the “noisy images”. CanopyCAM measured CC (CCCanopyCAM) were compared with CC obtained from a Li-COR Plant Canopy Analyzer (CCLAI). The average error between CCCanopyCAM and CCLAI was 2.3%, and RMSE and R2 were 2.95% and 0.99, respectively. In addition, maximum CC (CCmax) and duration of the maximum CC (tmax_canopy) were identified at each installation location using the generalized reduced gradient (CRG) algorithm with nonlinear optimization. An improvement of correlation was found between dry bean yield and combination of CCmax and tmax_canopy (R2 = 0.77, Adjusted R2 = 0.62) as compared to yield vs. CCmax (R2 = 0.58) or yield vs. tmax_canopy (R2 = 0.45). This edge-computing, IoT enabled capability of CanopyCAM, provided accurate CC readings which could be used by growers and researchers for different purpose

Development of a Scalable Edge-Cloud Computing Based Variable Rate Irrigation Scheduling Framework

Currently, variable-rate precision irrigation (VRI) scheduling methods require large amounts of data and processing time to accurately determine crop water demands and spatially process those demands into an irrigation prescription. Unfortunately, irrigated crops continue to develop additional water stress when the previously collected data is being processed. Machine learning is a helpful tool, but handling and transmitting large datasets can be problematic; more rural areas may not have access to necessary wireless data transmission infrastructure to support cloud interaction. The introduction of “edge-cloud” processing to agricultural applications has shown to be effective at increasing data processing speed and reducing the amount of data transmission to remote processing computers or base stations. In irrigation in particular, edge-cloud computing has so far had limited implementation. Therefore, an initial logic flow concept has been developed to effectively implement this new processing technique for VRI. Utilizing edge-cloud computer nodes in the field, autonomous data collection devices such as center pivot-mounted infrared canopy thermometers, soil moisture sensors, local weather stations, and UAVs could transmit highly localized crop data to the edge-cloud computer for processing. The edge computer Following the implementation of an irrigation strategy created by the edge-cloud computer with a machine learning model, data would be transmitted to the cloud (requiring transmission of only minimal model parameters), resulting in a feedback loop for continual improvement of the global model on the cloud (federated learning). VRI prescription maps from the SETMI model were used as the training data for training the machine learning model

Crop response to thermal stress without yield loss in irrigated maize and soybean in Nebraska

Thermal sensing provides rapid and accurate estimation of crop water stress through canopy temperature data. Canopy temperature is highly dependent on the transpiration rate of the leaves. It is usually assumed that any reduction in crop evapotranspiration (ET) leads to crop yield loss. As a result, an increase in canopy temperature due to a decrease in crop ET would indicate crop yield loss. This research evaluated the hypothesis that crop water stress could be detected using canopy temperature measurements (increased leaf temperature) from infrared thermometers (IRTs) before incurring crop yield loss. This would be possible in a narrow range when the photosynthesis rate (and carbon assimilation) is limited by solar radiation (energy-limiting water stress) while the leaf has abundant carbon dioxide for photosynthesis. Once photosynthesis becomes limited by carbon dioxide (carbon-dioxide-limiting water stress), then yield reduction would occur. In this field experiment, measured response variables included the integrated crop water stress index (iCWSI), ET, and crop yield for maize and soybean during the 2020 and 2021 growing seasons. The irrigation was applied at four different refill levels: rainfed (0%), deficit (50%), full (100%), and over (150%). The irrigation depth was prescribed using four different irrigation methods. The field was irrigated with a center pivot irrigation system, which was also used as a platform to mount IRT sensors. The iCWSI thresholds required for irrigation management were determined using the iCWSI dataset collected in 2020. The low, medium, and high iCWSI thresholds were 120, 150, and 180, respectively for maize and 110, 130, and 150, respectively for soybean. These thresholds should be updated with iCWSI data from future studies in this region to increase the credibility of the thresholds for irrigation management. The mean iCWSI values for consecutive days after a wetting event substantially increased with time for each irrigation level and a larger range in iCWSI values was observed among the irrigation levels after three days from a wetting event. The seasonal iCWSI for different levels were found to be negatively correlated with seasonal evapotranspiration for both years. The correlations between seasonal ET and crop yield were significant with the rainfed and deficit levels for maize (p-value \u3c 0.001) and soybean (p-value = 0.04) in 2020. The iCWSI and yield data for the fully watered plots indicated that thermal stress was detected using the sensing system without incurring yield loss (i.e., energy-limiting water stress). The ET and yield data for 2021 indicated that reduction in seasonal crop ET did not result in yield loss which also supported the hypothesis. Future studies should investigate whether this phenomenon of detecting crop water stress in an early stage without yield loss is observed in other climates and locations

Maize evapotranspiration, canopy and stomatal resistances, crop water productivity, and economic analysis for various nitrogen fertilizer rates under full irrigation, limited irrigation, and rainfed settings

Research was conducted for maize (Zea Mays L.) under various irrigation and nitrogen (N) fertilizer treatments at the University of Nebraska-Lincoln South Central Agricultural Laboratory located near Clay Center, NE in 2011 to 2014. The N fertilizer treatments were 0, 84, 140, 196, and 252 kg ha-1 and the irrigation treatments were full irrigation, limited irrigation (75% of full), and rainfed. The overall objectives of the study were to assess the differences in maize actual evapotranspiration (ET a), canopy resistance (rc), stomatal resistance (rs ), economic return, and crop water productivity as influenced by the imposed treatments as well as improve estimation techniques of the aforementioned by developing local crop coefficients (Kc), scaling up rs to rc, and developing a new function, f(SPAD), to account for N stress when modeling rs. Maize ETa ranged from 426 to 550, 411 to 535, 353 to 480, and 445 to 519 mm in 2011, 2012, 2013, and 2014, respectively. In almost all cases ETa increased with irrigation and, in general, ETa increased with increasing N fertilizer application. The greatest crop ET a rates and consequently, the highest Kc values as well as the lowest rc values occurred during the early reproductive growth stages. The Jarvis-Stewart (J-S) model for estimating rs was calibrated and evaluated using field measured rs from a model AP4 dynamic diffusion porometer. The measured rs values ranged from 37 to 2300 s m-1 in 2013 and 2014. For individual dates, the SPAD function decreased RMSD in the range of 1% (7/22/14) to 28% (6/17/13) and maintained or slightly improved Willmott\u27s index of agreement in all cases. The SPAD function improved model performance the most under the 0 kg N ha-1 treatments, which experienced the greatest N stress. A positive relationship existed between net income and crop water use efficiency (CWUE) and irrigation water use efficiency (IWUE). With consideration to IWUE, CWUE, and partial factor productivity of N, we recommend full irrigation under non-water limiting conditions and limited/deficit irrigation management strategies when water is limited, with N fertilizer rate not exceeding 196 kg ha-1 to achieve high economic return for the study area

Development and evaluation of ordinary least squares regression models for predicting irrigated and rainfed maize and soybean yields

Understanding the relationships between climatic variables and soil physical and chemical properties with crop yields on large scales is critical for evaluating crop productivity to make better assessments of local and regional food security, policy, land and water resource allocation, and management decisions. In this study, ordinary least squares(OLS) regression models were developed to predict irrigated and rainfed maize and soybean yields at the county level as a function of explanatory variables [precipitation (P), actual crop evapotranspiration (ETa), organic matter content (OMC), cation exchange capacity (CEC), clay content (CC), and available soil water capacity (ASW)] of the dominant soil type in each of the 93 counties in Nebraska. Models were developed for the statewide average dataset (state models) as well as for the four major climatic zones (zonal models). Spline interpolation was used to spatially interpolate all independent variables across all 93 counties. The results of the OLS state models showed a very good performance for predicting rainfed maize and soybean yields. For rainfed maize, about 73% of the variation in yield (RMSD = 867 kg ha-1) was explained by ETa alone, and 83% of yield variability (RMSD = 690 kg ha-1) was explained by the model Yield = f(ETa, P, ASW, CEC, CC). For rainfed soybean, about 69% of the variability (RMSD = 238 kg ha-1) was explained by ETa alone, and a maximum of 85% (RMSD = 164 kg ha-1) of the variability was explained by the model Yield = f(ETa, P, ASW, CEC, CC). No additional variation in yield was explained by adding OMC to the rainfed maize and soybean yield models. Less correlation was found between the predicted and observed yields for irrigated maize and soybean than for the rainfed yields for both crops. For irrigated maize and soybean, a maximum of 45% (RMSD = 533 kg ha-1) and 36% (RMSD = 218 kg ha-1) of the variability in yield was explained by the models Yield = f(ETa, P, ASW) and Yield = f(ETa, P, ASW, CEC, CC, OMC), respectively. For the rainfed crops, ETa played a major role in predicting yield, whereas P and ASW played a major role in predicting irrigated yields. ETa and P accounted for 96%, 73%, and 67% of the total explained variation in rainfed soybean yield for zones 2 (drier), 3, and 4 (wetter), respectively, whereas soil physical and chemical properties accounted for 4%, 27%, and 33%, respectively. Unlike rainfed conditions, irrigated maize and soybean yield predictions were improved by applying the zonal models rather than the state models

Impact of maize hybrid selection on water productivity under deficit irrigation in semiarid western Nebraska

The future economic and agricultural sustainability of semiarid western Nebraska will largely depend on more efficient utilization of the declining groundwater resources. The scope of this research was to evaluate the maize hybrid yield, water productivity (WP; i.e. grain yield produced per unit of water consumed), and irrigation water productivity (IWP; i.e. increase in grain yield per unit of irrigation water applied) across a range of semiarid climatic conditions (i.e. drought, normal, and wet) and irrigation treatments. Total of 13 maize hybrids were evaluated under full irrigation (FI), deficit irrigation (DI, receiving ~50% less irrigation water than FI), and dryland (DRY; rainfall only) at the University of Nebraska–Lincoln Brule Water Laboratory near Brule, Nebraska, in 2011 and 2012 and Bayer’s Gothenburg Water Utilization Learning Center near Gothenburg, Nebraska, in 2010 and 2011 (i.e. four site-years). Compared to FI, DI caused yield reduction of as much as 33% in a dry, 11% in a normal, and 2% in a wet year, resulting in consequently 22–47% improvement in IWP. Depending on site-year and irrigation level, a difference of up to 7.2 t ha–1, 3.6 kg m–3, and 5.9 kg m–3 was observed in yield, WP, and IWP, respectively, as a consequence of hybrid selection, with few top-performing hybrids yielding similarly under DI and FI in a normal and/or wet year. This study highlights the impact hybrid selection and DI have on crop water productivity (WP) and IWP as well as provides insight into strategies that can maintain productivity and profitability in water limited environments

Actual evapotranspiration and crop coefficients of irrigated lowland rice (\u3ci\u3eOryza sativa\u3c/i\u3e L.) under semiarid climate

Lowland irrigated rice is the predominant crop produced in the Senegal River Valley characterized by very low annual rainfall, high temperatures, and low relative humidity. The Senegal River is shared by Senegal, Mali, Mauritania, and Guinea, and serves as the main source of irrigation water for the adopted double rice cropping system. Developing appropriate resource management strategies might be the key factor for the sustainability of rice production in the region. This study aims to estimate rice seasonal evapotranspiration (ETa), irrigation water requirement, and to develop rice growth stage specific crop coefficients (Kc) to improve rice water productivity. Field experiments were conducted during the hot and dry seasons in 2014 and 2015 at the AfricaRice research station at Fanaye in Senegal. Irrigation water inputs were monitored and actual crop evapotranspiration was derived using the water balance method. Daily reference evapotranspiration (ETo) was estimated using the Penman-Monteith equation and the weather variables were collected at the site by an automated weather station. The results showed that the ETo during the hot and dry season from February 15th to June 30th varied from 4.5 to 9.9 mm and from 3.7 to 10.8 mm in 2014 and 2015, respectively, and averaged 6.8 mm d-1 in 2014 and 6.6 mm d-1 in 2015. The seasonal irrigation water amount for the transplanted rice was 1110 mm in 2014 and 1095 mm in 2015. Rice daily ETa varied from 4.7 to 10.5 mm in 2014 and from 4.4 to 10.5 mm in 2015 and averaged 8.17 mm in 2014 and 8.14 mm in 2015. Rice seasonal ETa was 841.5 mm in 2014 and 855.4 mm in 2015. The derived rice Kc values varied from 0.77 to 1.51 in 2014 and 0.85 to 1.50 in 2015. Rice Kc values averaged 1.01, 1.31, and 1.12 for the crop development, mid-season and late season growth stages, respectively. The Kc values developed in this study could be used for water management under rice production during the hot and dry season in the Senegal River Valley