ABSTRACT There is limited integrated evaluation of nitrogen (N) and irrigation management strategies for improving maize yield and productivity using crop models. The effects of N and irrigation on maize yield and productivity under centre pivot (CPI) and subsurface drip irrigation (SDI) using the CERES‐Maize model were investigated. N treatments were (i) traditional N (TN) (pre‐plant), (ii) non‐TN (NT1) with three applications and (iii) non‐TN (NT2) with four applications. The irrigation treatments were (i) full irrigation treatment (FIT), (ii) 80% FIT, (iii) 60% FIT and rainfed (RFT). Calibration had strong agreements in CPI and SDI [ RMSE n = 6%; R 2 = 0.99 for yield; 11% and 0.98 for crop evapotranspiration ( ET c ), respectively]. ET c ‐based water productivity ( WP ET ) had a percent error ( P e ) of −7% (CPI). For SDI, the model performed similarly ( RMSE n = 8%; R 2 = 0.99 for yield, 11% and 0.99 for ET c , and a P e of −5% for WP ET ). The CERES‐Maize model overestimated under RFT (36% error for yield and 19% for ET c ). The SDI at FIT with TN was the most effective strategy, with the highest financial return ($2098/ha) and a mean Gini difference of $1809/ha. While CERES‐Maize is a robust tool for identifying economically optimal strategies, its declined accuracy under water‐stressed conditions needs further investigation.

Accurate estimation of reference evapotranspiration (ETo) is essential for effective irrigation planning and water resource management, particularly in regions with limited meteorological data. This study evaluates calibration strategies and develops a regional calibration framework for the Hargreaves–Samani (HS) equation to improve its performance under arid and semi-arid conditions. A 13-year dataset (2008–2021) from 24 synoptic stations across Fars Province, Iran, was used to evaluate the original and ten modified HS equations against the FAO24-Radiation method adopted as the reference model. The original HS equation systematically underestimated ETo, with a normalized root mean squared error (NRMSE) and index of agreement (d) of 0.38 and 0.81, respectively. Although several modified HS equations improved estimation accuracy, calibration of the empirical coefficients produced the most consistent improvement. Notably, calibrating only the primary coefficient (“a”) achieved comparable performance to multi-parameter (“a, b, and c”) calibration, supporting that the use of a simplified approach is sufficient for reliable estimation. Based on this finding, a generalized empirical equation was developed to estimate the calibrated coefficient “a” from mean relative humidity, elevation, and the De Martonne aridity index, enabling spatial application of the HS model across the study region without site-specific calibration while preserving its temperature-only operational simplicity. Independent validation confirmed the robustness of the calibrated model (NRMSE = 0.16; d = 0.97). The proposed regional calibration framework provides a practical and computationally efficient approach for improving HS-based evapotranspiration estimation in arid and semi-arid regions with similar climatic characteristics and supports irrigation planning and water resources management where operational meteorological data are limited.

Sweet corn (Zea mays convar. saccharata var. rugosa) is an important dietary commodity and is increasingly consumed in many applications. Despite its increased value worldwide, some of the fundamental sweet corn productivity variables are unknown. This research quantified and analyzed sweet corn ear yield, crop evapotranspiration [using FAO56 method (ETc) and soil-water balance method (ETc_SWB)], water productivity (WP), transpiration (Tr), soil evaporation (E), ETc-yield production function (ETYPF), soil-water extraction (SWE) and developed basal crop coefficients (Kcb) as a function of cumulative growing degree days (Sigma GDD) for two growing seasons. In 2015, irrigated and rainfed yields were 9256 and 7943 kg/ha, respectively. In 2016, irrigated and rainfed yields were 8683 and 7342 kg/ha, respectively. Similar values of maximum Kcb occurred at different Sigma GDD between the seasons. Seasonal ETc were 462 mm in 2015 and 419 mm in 2016. Seasonal Tr was 316 mm in 2015 and 275 mm in 2016. Seasonal E was 143 mm in 2015 and 146 mm in 2016. The FAO56 ETc and ETc_SWB correlated strongly on a weekly time step [R2= 0.81, root mean squared difference (RMSD)= 6.3 mm/wk; slope= 0.99]. The irrigated WP values were 26.5 and 32.1 kg/m3 in 2015 and 2016, respectively. On a twoseason average basis, rainfed sweet corn extracted 52, 33, 9, 8 and 5% of the seasonal total soil-water from the 0-030, 0.30-0.60, 0.60-0.90, 0.90-1.20 and 1.20-1.50 m soil layers, respectively; and irrigated sweet corn extracted 56, 29, 10, 7 and 3% of the seasonal total soil-water from the same soil layers, respectively.

Long-term economic analyses of variable rate irrigation (VRI) strategies were performed compared with uniform irrigation management (UIM) in a reference production field using the AquaCrop model. Five strategies to trigger irrigation were as follows: (Field Capacity-VRI, Driest Soil Trigger-VRI, Water Mining-VRI, Conventional Uniform Irrigation Management (CUIM) and field-averaged uniform method (Aggregated Uniform Irrigation Method, AUIM). Thirteen field distribution models were developed by varying the reference field's soil textures (the reference field had four different soil textures) to model field variability. Analyses were based on three cost factors (100%, 75%, and 50% of the current costs of VRI). Costs, feasibility, and profits were calculated considering irrigation strategies and field soil distributions. VRI was not feasible at present costs because an average annual irrigation reduction of 2.4% was not able to justify the 4% yearly loss as compared with CUIM, considering capital and operational costs. AUIM is a feasible strategy and showed $2907 annual savings with CUIM. VRI was effective in the field areas where water mining is practical, that is, fields with variability in high water holding capacity soils covering at least 60% of the field. A reduction of at least 25% in the initial costs was considered essential for VRI to be economically beneficial. Des analyses & eacute;conomiques & agrave; long terme des strat & eacute;gies d'irrigation & agrave; d & eacute;bit variable (VRI) ont & eacute;t & eacute; r & eacute;alis & eacute;es par comparaison avec la gestion d'irrigation uniforme (UIM) sur une parcelle de production de r & eacute;f & eacute;rence, en utilisant le mod & egrave;le AquaCrop. Les cinq strat & eacute;gies de d & eacute;clenchement de l'irrigation & eacute;taient les suivantes: (capacit & eacute; au champ-VRI, seuil de sol le plus sec-VRI, extraction d'eau-VRI, gestion conventionnelle uniforme de l'irrigation (CUIM) et m & eacute;thode uniforme moyenn & eacute;e sur le champ (m & eacute;thode d'irrigation uniforme agr & eacute;g & eacute;e, AUIM). Treize mod & egrave;les de distribution de la parcelle ont & eacute;t & eacute; d & eacute;velopp & eacute;s en faisant varier les textures du sol de la parcelle de r & eacute;f & eacute;rence (la parcelle de r & eacute;f & eacute;rence pr & eacute;sentait quatre textures de sol diff & eacute;rentes) afin de mod & eacute;liser la variabilit & eacute; de la parcelle. Les analyses se sont appuy & eacute;es sur trois facteurs de co & ucirc;t (100, 75 et 50% des co & ucirc;ts actuels de la VRI). Les co & ucirc;ts, la faisabilit & eacute; et les b & eacute;n & eacute;fices ont & eacute;t & eacute; calcul & eacute;s en tenant compte des strat & eacute;gies d'irrigation et des distributions du sol au sein de la parcelle. La VRI n'& eacute;tait pas faisable aux co & ucirc;ts actuels, en raison d'une r & eacute;duction moyenne annuelle de l'irrigation de 2,4% qui ne pouvait pas justifier la perte annuelle de 4% par rapport & agrave; la CUIM, compte tenu des co & ucirc;ts d'investissement et d'exploitation. L'AUIM est. consid & eacute;r & eacute; une strat & eacute;gie faisable et permet de r & eacute;aliser des & eacute;conomies annuelles de 2907 $ par rapport au CUIM. La VRI s'est. montr & eacute;e efficace dans les zones o & ugrave; l'extraction d'eau est. pratique, c'est-& agrave;-dire dans les parcelles pr & eacute;sentant une variabilit & eacute; de sols & agrave; forte capacit & eacute; de r & eacute;tention d'eau couvrant au moins 60% de la superficie. Une r & eacute;duction d'au moins 25% des co & ucirc;ts initiaux a & eacute;t & eacute; estim & eacute;e indispensable pour que la VRI soit b & eacute;n & eacute;fique du point de vue & eacute;conomique.

While watermelon is an important commodity in the United States and worldwide, some of the fundamental watermelon productivity indices (crop evapotranspiration [ETc], transpiration [T-r], evaporation [E], ET-yield production functions [ETYPF], basal crop coefficients [K-cb], crop water productivity [CWP], soil-water extraction [SWE]) have not been quantified. These variables were quantified for three different watermelon varieties (Quetzali, Sangria and Top Gun) in two seasons. The 2-year average yields were 34,061, 27,967 and 26,046 kg/ha for Sangria, Top Gun and Quetzali, respectively. All yields were significantly different (p < 0.05) from each other. The seasonal total ETc was 524 and 457 mm, T-r was 381 and 321 mm and E was 146 and 140 mm in 2015 and 2016, respectively. The two-season average K-cb values were 0.40, 0.53, 0.94, 0.93, 0.63 and 0.40 for planting-germination, germination-vegetative, vegetative-flowering, flowering-fruit initiation, fruit initiation-fruit growth and fruit growth-ripening stages, respectively. The ETYPF was linear and strong (slope = 0.299; R-2 = 0.90). The two-season average CWP was 6.5, 8.0 and 6.9 kg/m(3) for Quetzali, Sangria and Top Gun, respectively. It can be expected that 55%-57% of the seasonal total watermelon SWE will occur in the 0- to 60-m soil layer and 77%-81% will occur in the 0- to 120-cm soil layer.

Soybean growth, yield, crop evapotranspiration (ETc) and crop water use efficiency (CWUE or crop water productivity, CWP) under different irrigation levels in three different soil types in the same field were investigated concurrently. Treatments in each soil type were: (i) variable rate irrigation (VRI), (ii) fixed rate full irrigation (FR-1 '') and (iii) fixed rate limited irrigation (FR-0.75 ''). There was not enough evidence suggesting the superiority of VRI over FRI-1 '' or FRI-0.75 '' in terms of improving yield or CWUE. Leaf area index (LAI) and plant height were stronger functions of soil types than irrigation treatments. Growing season cumulative grass-reference evapotranspiration (ETo) and cumulative precipitation were 629 and 489 mm, respectively, in 2018; and 589 and 551 mm, respectively, in 2019. Variations in yield among irrigation treatments for both seasons were not significant (p > 0.05). Soil type, rather than irrigation treatments, explained variation in yield with statistical significance (p < 0.05). Soil types had substantial impact on ETc and CWUE. Since spatial variability in soil properties has a profound impact on soybean growth, yield, ETc and CWUE, soil variability in horizontal and vertical domain must be considered for developing accurate management zones and prescriptions for VRI, and for in-season VRI, FRI and limited irrigation management for successful and effective operations.

The effects of different irrigation methods (center pivot [CPI], subsurface drip [SDI] and furrow irrigation [FI]) and levels (full irrigation treatment [FIT], 80% FIT, 60% FIT and rainfed) on yield, crop evapotranspiration (ETc), ET-water productivity (WPET) and drought stress index for leaf expansion (SIE) and photosynthesis (SIP) of maize were investigated using field data and the CERES-Maize model. The irrigation method and level had a significant (p < 0.05) effect on the productivity variables. The calibration results were in good agreement between the simulated and measured yields (RMSEn = 6.9%; RMSE = 0.97 t/ha, R-2 = 0.99) and ETc (RMSEn = 11.3%; RMSE = 54.5 mm; R-2 = 0.96), although the model systematically overestimated ETc. The yield error ranges were 0.9%-18% (CPI), -0.05%-25% (SDI) and 2%-16% (FI). The ETc errors were 8%-14% (CPI), 72%-14% (SDI), 5% (FI-FIT) and 19% (FI-rainfed). The validation results were reasonably accurate for yield (RMSEn = 14.3%; RMSE = 1.98 t/ha; R-2 = 0.91) and ETc (RMSEn = 11.3%; RMSE = 54.2 mm; R-2 = 0.68), with errors of -36-36% (CPI), -0.2-14% (SDI) and 9%-60% (FI)., with rainfed having the highest errors. The ETc validation error ranges were -4%-10% (CPI), 3%-19% (SDI) and -5%-22% (FI). WPET simulations had moderate calibration accuracy (RMSEn = 8%; R-2 = 0.94) and acceptable validation accuracy (RMSEn = 12%; R-2 = 0.65). The drought stress indices were strongly (but inversely) correlated with yield.

The performance and impacts of the sprayable and degradable bio-based polymer (BBP), which was prepared from renewable materials such as poultry feathers and low-grade woody biomass, on soil health (quality); weed germination, growth, and pressure; soil temperature; soil-water; soil organic matter content (SOMC), nitrogen content; and soybean (Glycine max (L.) Merr.) productivity was investigated. The treatments included soybean+weed. with and without BBP applications. The weed was giant foxtail (Setaria faberi Hermm.). The soil-water content was up to 13 mm higher (p<0.05) in BBP treatments than in the control, indicating the soil-water conservation effect of BBP. The BBP application did not negatively affect soybean emergence; soybeans in both BBP and control treatments emerged on the same day. However, weed emergence was delayed by 3-5 days in the BBP treatment compared with the control. The soil temperature in the control was 3 degrees C to 6.7 degrees C greater than the BBP treatment's soil temperature, indicating the temperature moderation effect of the BBP during extreme conditions, especially in hot and dry summer periods. Soybean dry matter and height with BBP application were significantly greater, and the weed count was significantly (p<0.05) lower than that without BBP. Soybean dry matter with the BBP application was 32% greater (p<0.05) than that without the BBP application (control). The BBP application reduced the variation in weed count and weed dry matter production by 78% and 67%, respectively. The average plant height for the BBP-applied plants was 821 mm, whereas it was 691 mm for the control. Average weed dry matter with BBP application was significantly (p<0.05) lower than that without BBP application. The plots with BBP application had significantly (p<0.05) lower weed numbers (18.3 weeds per plot) than those without BBP application (47.3 weeds per plot). The BBP application significantly increased soil organic matter and nitrogen content compared with the control throughout this experiment. This initial research indicated that BBP can provide beneficial conditions for plant growth and production while partially controlling S. faberi weed.

Highlights Crop N removal response to N inputs shows diminishing N removal beyond 163.08 kg ha-1. N inputs at which diminishing returns are observed have increased. N inputs at which diminishing returns are observed are specific to crop belts. Proportion of counties that show N inputs exceeding the optimal level has increased. ABSTRACT. The response of nitrogen (N) removal by crops to an increase in fertilization strongly determines the profitability and sustainability of agricultural systems and informs nutrient management decisions at the producer level. These response functions are analyzed for agronomic and economic optima achieved at field scales for evaluating production, economic, and environmental goals. However, such assessments are lacking for entire regional agroecosystems to allow understanding of the response of N removal collectively across all crops grown during the year (N removal ) to total N fertilization (i.e., all manageable N sources, N in ) historically. Here, we address this knowledge gap by leveraging a large-scale N budget and statistical techniques to characterize space-time variability and trends in historical (1987–2016) county-level N removal , N in , and nitrogen use efficiency (NUE) across the conterminous U.S. (CONUS). We intend to evaluate crop belt-specific characteristics of diminished returns in N removal to N in response and change over time. N in , N removal , and NUE were subject to drastic spatial variation in long-term mean values, interannual variability, and long-term change, which were quantified and mapped to understand their spatiotemporal distributions. Pooled across all counties and years, N removal shows diminished returns when N in reached 163 kg ha -1 (N in, bp ). Upon quantifying and analyzing year-specific diminished returns, we found that N in, bp has increased during 1987–2016, and so has the NUE achieved prior to attaining diminished returns. The proportion of counties (6%–22%) where N in exceeds N in, bp also increased, and counties that repeatedly demonstrated such exceedance during 1987–2016 were identified. Values of N in,bp are specific to crop belts within the U.S., the majority of which also show increased N in, bp over time. Specifically, barley, beans, and sugarbeets (198 kg ha -1 ), and alfalfa and barley (190 kg ha -1 ) belts showed notably greater N in,bp relative to the national mean (163 kg ha -1 ), while N in,bp for corn grain and soy belts was similar to the national mean. Overall, these findings represent a comprehensive assessment of how systems-level N removal across U.S. agriculture has historically responded to change in N in , a prerequisite for guiding mitigation and adaptation policy and efforts. Keywords: Fertilizer, Manure, Nitrogen cycle, Nitrogen use efficiency, Yield.

This research investigated soybean soil water dynamics under different irrigation levels in three different soil types in the same field concurrently. Treatments imposed in each soil type were: (i) variable-rate irrigation (VRI), (ii) fixed-rate full irrigation (FRI-1 '' or FRI-25.4 mm) and (iii) fixed-rate limited irrigation (FRI-0.75 '' or FRI-19 mm). In 2018, VRI received 75% less water than FRI-1 '' and received 49% less water than FRI-0.75 ''. In 2019, VRI received 100% more irrigation than FRI-1 '' and 41% less than FRI-0.75 ''. Soil water dynamics of each treatment in the same soil and between the soils exhibited substantial interannual variations. Soil type had substantial and greater impact on soil moisture dynamics than irrigation treatments. Total available water (TAW), dry spell and antecedent soil moisture were impacted to a greater extent by the spatial soil properties than irrigation treatments. The range of field capacity (FC), permanent wilting point (PWP), TAW, dry spell soil moisture and antecedent soil moisture quantified for each soil type spatially and temporally in the same research field with respect to soil moisture dynamics and impacts on irrigation requirements for different irrigation management strategies provide a beneficial scope of understanding the effects of these spatially variable soil properties on water management. The research also provides substantial evidence in terms of the critical importance of detailed quantification, analyses and understanding of the soil properties that must be considered for successful implementation of VRI technology.

Highlights NUE response to N addition is dependent on N source (fertilizer, manure, and biological fixation). Random forest models captured 71% and 47% NUE variance for CONUS and global croplands. Contribution from biological N fixation was most important for explaining NUE variance, followed by manure and fertilizer contributions. ABSTRACT. Nitrogen use efficiency (NUE) is a useful indicator of the tradeoffs among cropland harvest nitrogen (N) and total N fertilization. Total N fertilization can be fulfilled by different sources depending on local availability, livestock production, land use and crop distribution, and economics, all of which change drastically in space and time. While NUE assesses crop harvest N response to total N fertilization, it typically does not distinguish between N fertilization sources, and thus little is known on how varying contributions from diverse N inputs impact NUE achieved in a region and year. Here, we use long-term (1961–2020) N budgets combined with random forest modeling to address this knowledge gap for global croplands, with a finer spatial emphasis on conterminous United States (CONUS) croplands. Random forest models using fractional fertilization contributions (F fert , F manure , and F bnf for synthetic fertilizers, livestock manure, and biological N fixation, respectively) and captured 71% and 47% of space-time variance in NUE for CONUS and global croplands, respectively. F bnf was the most important predictor for explaining variance in county/country-year NUE, followed by F manure , and F fert . Contributions from each of the input sources exerted distinct controls on NUE through its observed ranges and these controls were visualized using partial dependence plots for NUE. The models establish that regions and years where a higher proportion of total N fertilization is met by biological N fixation (relative to fertilizers and manure) have higher NUE. Overall, our findings improve understanding of how NUE may be optimized by managing diverse N sources with the aim of meeting economic and sustainability goals. Keywords: Chemical fertilizers, Livestock, Nitrogen cycle, Nutrient budgets, Nutrients.

Water scarcity, climate variability, and increasing competition for water resources pose significant challenges to global food production. Irrigation remains a critical tool for stabilizing and enhancing agricultural productivity, particularly in arid and semi-arid regions, but must be managed more efficiently to meet future food demands with limited water supplies. Precision irrigation integrates site-specific water application, advanced sensing technologies, decision support systems, and automation to apply the right amount of water at the right time and place. This report examines the principles, technologies, and applications of precision irrigation across gravity, sprinkler, and microirrigation systems. It reviews variable rate irrigation, soil- and plant-based sensing, evapotranspiration monitoring, irrigation scheduling tools, and complementary technologies such as fertigation and data-driven decision support systems. The report also evaluates the agronomic, environmental, and economic benefits of precision irrigation, as well as current adoption levels and barriers to wider implementation. By synthesizing recent research and practical experiences, this report highlights how precision irrigation can improve water productivity, enhance crop performance, and increase resilience to climate change. It concludes with an assessment of future research, extension, and education needs required to accelerate adoption and maximize the benefits of precision irrigation in modern agriculture.

Palmer amaranth (Amaranthus palmeri S. Watson) is a major biotic constraint in agronomic cropping systems in the United States. While crop-weed competition models offer a beneficial tool for understanding and predicting crop yield losses, within these models, certain weed biological characteristics and their responses to the environment are unknown. This limits understanding of weed growth in competition with crops under different irrigation methods and how competition for soil moisture affects crop growth parameters. This research measured the effect of center-pivot irrigation (CPI) and subsurface drip irrigation (SDI) on the actual evapotranspiration (ETa) of A. palmeri grown in maize (Zea mays L.), soybean [Glycine max (L.) Merr.], and fallow subplots. Twelve A. palmeri plants were alternately transplanted 1 m apart in the middle two rows of maize, soybean, and fallow subplots under CPI and SDI in 2019 and 2020 in south-central Nebraska. Maize, soybean, and fallow subplots without A. palmeri were included for comparison. Soil-moisture sensors were installed at 0-0.30, 0.30-0.60, and 0.60-0.90-m soil depths next to or between three A. palmeri and crop plants in each subplot. Soil-moisture data were recorded hourly from the time of A. palmeri transplanting to crop harvest. The results indicate differences in A. palmeri ETa between time of season (early, mid-, and late season) and crop type across 2019 and 2020. Although irrigation type did not affect subplot data, the presence of A. palmeri had an impact on subplot ETa across both years, which can be attributed to the variable relationship between volumetric soil water content (VWC) and ETa throughout the growing season due to advancing phenological stages and management practices. This study provides important and first-established baseline data and information about A. palmeri evapotranspiration and its relation to morphological features for future use in mechanistic crop-weed competition models.

Context Platforms and instrumentation for Field High-Throughput Plant Phenotyping (FHTPP) are well developed to measure important traits for crop breeding and agronomic studies. However, the research has focused on morphological and spectral traits; and approaches to estimate major physiological processes such as evapotranspiration (ET) for small experimental plots are lacking. Objective In this study, we put forward a new analytical framework to estimate plot-scale ET by integrating frequent phenotyping data (multispectral and thermal infrared images, canopy reflectance, and LiDAR point clouds) from a FHTPP system (known as NU-Spidercam), the weather data, a simplified two-source energy balance model, and reference ET and crop coefficient calculation. Methods The new plot-scale ET method was tested on five field experiments involving maize and soybean crops over two growing seasons, with the different treatment levels of irrigation water. Estimated plot-scale ET was accumulated across the growing reason for each plot, and its association with grain yield was investigated with regression analysis. Results The result showed that plot-scale accumulated ET captured the seasonal trend of plot water use and clearly differentiated the irrigation treatments. Strong linear correlations were observed between plot-scale ET and grain yield, with R2 values ranging from 0.35 to 0.93 (average R2 = 0.71). Plot-scale ET appeared to be a more steady and stronger predictor of grain yield across the seasons than several other morphological and spectral traits including crop height, green pixel fraction, canopy temperature depression, and red-edge normalized difference vegetation index. Conclusion High spatial and temporal resolution of the field phenotyping data, along with the new analytical framework reported, successfully estimated ET at small plot scale, which is difficult to achieve with other systems or methods. Significances Our work of estimating ET at the plot-scale can be adopt to other ground-based platforms and drones, thus empowers physiologists, breeders, and agronomists for high-throughput phenotyping of water-use related traits and drought response evaluation.

Nebraska is the number one producer of food-grade white corn in the United States. Pollen-mediated gene flow (PMGF) from genetically engineered high alpha-amylase corn, known as Enogen corn, to food-grade white corn can have undesirable outcomes. Alpha-amylase can convert starch in white corn to sugar during or after its processing, degrading the quality of processed products. Thus, proximity to Enogen corn puts white corn production at risk. The objectives of this study were to evaluate the PMGF from herbicide-resistant yellow corn to food-grade white corn and assess the significance of wind speed and direction. Field experiments were set up using a Nelder-wheel design in 2021 and 2022 in Nebraska, with yellow field corn as pollen donor in the center and white corn surrounding it as pollen receptor. At the end of the season, samples of white corn cobs were collected up to 50 and 70 m from four cardinal and ordinal directions, respectively. PMGF was detected by counting the number of yellow kernels on the white cobs. More than 4 million kernels were screened and the highest frequency of PMGF (0.0621-0.1950) was detected at the nearest distance (1 m). PMGF decreased exponentially with distance; however, it was still observed (0.0020-0.0032) at the greatest distance (70 m) evaluated in this study. Wind profile played a significant role in PMGF. Wind frequency (r = 0.58 <= 0.86) and wind run (r = 0.36 <= 0.95) were moderately to strongly correlated with PMGF in 2021 and 2022, respectively. The results are concerning for white corn growers due to the coexistence of Enogen corn and food-grade white corn. The pollen-mediated gene flow (PMGF) from yellow corn to white corn was greatest at the nearest distance (1 m). The exponential decay model was the best fit to explain PMGF with distance from the yellow corn. Wind parameters such as wind speed, direction, and frequency affected PMGF, signifying the role of wind in PMGF in corn.

Highlights Hourly sap flow measured in co-located and identically managed maize, sorghum, and soybean closed canopies. PAR, VPD, and ET r were strongly correlated to transpiration (T) normalized by LAI. Negative response of T to high VPD (3-4 kPa) was observed for maize and sorghum. Counterclockwise hysteresis observed for diurnal T-VPD and T-PAR. MLR models were developed to estimate T using VPD and PAR. Abstract. Transpiration (T) dominates terrestrial hydrological fluxes and is strongly coupled with vegetation productivity and water use efficiency across different biomes, including agricultural systems. Studying how T in field crops responds to environmental variability has important implications to inform and predict agroecosystems’ response to a changing environment. However, comparative T rates among major field crops remain unknown in many regions where drought severity and limited freshwater availability are projected, such as the Central U.S. Plains. We address this knowledge gap by monitoring and characterizing hourly T for field-grown maize, grain sorghum, and soybean crops under the same weather, soil, and management regimes using sap flow sensors. The relationships among crop-specific T and air temperature (Tair), relative humidity (RH), wind speed (u2), vapor pressure deficit (VPD), incoming shortwave radiation (Rs), photosynthetically active radiation (PAR), net radiation (Rn), and grass- and alfalfa-reference evapotranspiration (ETo, ETr) were investigated. T normalized by leaf area index (T LAI-1) was most correlated with PAR (r=0.88), ETr (r=0.84), and VPD (r=0.81). Mean sensitivity of T LAI-1 to unit change in Tair, Rs, PAR, Rn, u2, RH, VPD, and ETr for maize and sorghum was 88% and 59% greater than that of soybean, respectively. All crops showed non-linear T LAI-1 response to increasing VPD, and a negative response of T LAI-1 to VPD was observed in the 3.0-4.0 kPa VPD range for maize and sorghum. Each crop demonstrated a counterclockwise hysteresis effect to diurnal T-VPD and T-PAR, which was 177% and 87% greater (for T-VPD) and 44% and 17% greater (for T-PAR) in maize and sorghum, respectively, than soybean. Transpiration has rarely been measured in row crops, especially in a comparative fashion, and thus, the concurrent T dynamics and their environmental controls characterized in this research are of critical importance. These data can be instrumental for quantitatively assessing change in true crop water use (transpiration) and thus crop suitability under projected environmental change. Keywords: Hysteresis, Photosynthetically active radiation, Reference evapotranspiration, Sapflow, Vapor pressure deficit.

Volunteer corn (Zea mays L.) is a competitive weed in corn-based cropping systems. Scientific literature does not exist about the water use of volunteer corn grown in different crops and irrigation systems. The objectives of this study were to characterize the growth and evapotranspiration (ETa) of volunteer corn in corn, soybean [Glycine max (L). Merr.], and sorghum [Sorghum bicolor (L.) Moench] under center-pivot irrigation (CPI) and subsurface drip irrigation (SDI) systems. Field experiments were conducted in south-central Nebraska in 2021 and 2022. Soil moisture sensors were installed at depths of 0 to 0.30, 0.30 to 0.60, and 0.60 to 0.90 m to track soil water balance and quantify seasonal total ETa. Corn was the most competitive, as volunteer corn had the lowest biomass, leaf area, and plant height compared with the fallow. Soybean was the least competitive with volunteer corn, as the plant height, biomass, and leaf area of volunteer corn in soybean were similar to fallow at 15, 30, 45, and 60 d after transplanting (DATr). Averaged across crop treatments, irrigation type did not affect volunteer corn growth at 15 to 45 DATr. Soil water depletion and ETa were similar across crop treatments with and without volunteer corn, as water was not a limiting factor in this study. The ETa of volunteer corn was the highest in soybean (623 mm), followed by sorghum (622 mm), and corn (617 mm) under CPI. The SDI had higher irrigation efficiency, because without affecting crop yield, it had 3%, 6%, and 8% lower ETa in soybean (605 mm), sorghum (585 mm), and corn (571 mm), respectively. Although soil water use did not differ with volunteer corn infestation, a soybean yield loss of 27% was observed, which suggests that volunteer corn may not compete for moisture under fully irrigated conditions; however, it can impact the crop yield potential due to competition for factors other than soil moisture.

Grain yield, irrigation-yield production functions (IYPFs), evapotranspiration-yield production functions (ETYPFs), total soil water-yield production function (TSWYPF), crop evapotranspiration (ETc), and basal ET (ETb) response of subsurface drip-irrigated (SDI) maize were investigated under full irrigation treatment (FIT), 75 % FIT, 50 % FIT, and rainfed (RF). Yield response to irrigation differed significantly (P<0.05) between the treatments with FIT having the highest grain yield, followed by 75 % FIT, 50 % FIT, and RF in all growing seasons. There was a 14, 6, and 12 % yield reduction in 75 % FIT, 50 % FIT, and RF with respect to FIT, respectively. FIT had the highest ETc, followed by 75 % FIT, 50 % FIT, and RF. ETc reduction with 75 % FIT, 50 % FIT and RF with respect to FIT had similar reductions between the years. Under these experimental conditions, ETc of SDI-irrigated maize can be expected to be reduced by 5.2 % (25 mm), 13 % (65 mm), and 26 % (130 mm) with the limited irrigation (75 % FIT and 50 % FIT) and RF, respectively. The amount of irrigation water required for maximum grain yield varied between the growing seasons as a function of climatic conditions (262, 225, and 173 mm in 2004, 2005, and 2006, respectively). Based on the IYPFs, a 25.4 mm of irrigation application resulted in 0.061, 0.063, and 0.066 t/ha yield increase (beyond the intercept) in 2004, 2005, and 2006, respectively, with a 3-yr average of 0.063 t/ha. A 25.4 mm of irrigation application resulted in 15.6, 16.0, and 13.7 mm of increase in ETc (beyond the intercept) in 2004, 2005, and 2006 seasons, respectively, with a 3-yr average of 15.1 mm. On a three-year average basis, 10.7, 29.1, and 67 % yield reduction in 75 % FIT, 50 % FIT, and RF treatments with respect to FIT can be expected under these climate, soil-water, and crop management conditions with SDI-irrigated maize. A strong dependence of the ETYPF slopes on RF treatment’s yield was observed. ETb had substantial inter-annual variation as 356, 230, and 315 mm in 2004, 2005, and 2006, respectively. ETb was strongly and positively correlated (R2=0.99) with the seasonal precipitation and strongly, but negatively correlated (R2=0.89) with seasonal cumulative thermal unit (Growing Degree Days). Based on the pooled ETYPFs, a 25.4 mm of ETc resulted in 1.86, 1.72, and 2.61 t/ha grain yield (beyond the intercept) in 2004, 2005, and 2006, respectively, with a seasonal average of 2.1 t/ha. Data and information of this research can provide guidance for irrigation professionals, managers, advisors, engineers, agronomists, economists, and other professionals and can be incorporated into the planning, forecasting, allocating and managing of water resources availability-demand-actual use analyses and decisions to enhance crop production efficiency.

The effects of irrigation and nitrogen (N) on grain yield, actual crop evapotranspiration (ETa), N uptake efficiency (NUE), partial factor productivity of N (PFPN), and N utilization efficiency (NUtE) of maize (Zea mays L.) under centre pivot (CP), subsurface drip (SDI) and gravity (furrow) irrigation (FI) were quantified. Field experiments were conducted in two growing seasons under the full irrigation treatment (FIT), limited irrigation treatments of 80% FIT and 60% FIT and rainfed treatment (RFT). The highest NUE was observed under limited irrigation in the order of 80% FIT > FIT & GE; 60% FIT in non-traditional treatment-1 (NT-1) in both the CP and SDI methods. NUtE increased with irrigation amount. There were strong correlations between NUtE and grain yield regardless of irrigation method. ETa increased with N amount, and the slopes of these relationships varied substantially between irrigation and N levels and irrigation methods. PFPN increased with irrigation amount across the irrigation methods and N treatments. A higher PFPN was observed in the lowest total applied N plots and in-season split N application treatments. The pooled data for the CP and SDI methods showed significantly higher PFPN (20.3%) than the FI method, whereas the PFPN values in the CP and SDI methods were equal.

The CERES-Maize model performance was investigated in simulating maize phenology, grain yield, soil–water, evapotranspiration, and water productivity under different irrigation and nitrogen (N) levels under a variable rate lateral (linear)-move sprinkler irrigation system. The irrigation levels were rainfed, full irrigation treatment (FIT) and 75