Optimal internal nitrogen use efficiency for irrigated cotton in the southwestern United States

Bronson, K. F.

doi:10.1002/agj2.20674

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…Following the recommendations of Bronson (2021), liquid urea ammonium nitrate (UAN 32-0-0) was uniformly applied in three split applications in both growing seasons, amounting to seasonal nitrogen application rates of 149 kg N ha -1 each year. A fertigation trailer, which included a fertilizer tank, metering pump, and gasoline-powered generator, was hitched to the lateral-move irrigation machine, and fertilizer was injected into the overhead irrigation pipe.…”

Section: Fertilizer Managementmentioning

confidence: 99%

Combining Soil Water Content Data with Computer Simulation Models for Improved Irrigation Scheduling

Thorp

2023

Journal of the ASABE

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Highlights Stand-alone irrigation scheduling models were compared to models combined with soil water content data. Use of soil water content data reduced irrigation recommendations compared to stand-alone models. Cotton fiber yield and water productivity were often not significantly impacted by the reduced irrigation amounts. Weather station aridity and other measurement uncertainties must be addressed to improve the methodology. Abstract. Irrigation scheduling models can be used to guide irrigation management decisions, but their simulations often deviate from reality. Combining in-season field data with models may improve the simulations, leading to better irrigation decisions and improved agronomic outcomes. The objective of this study was to evaluate cotton fiber yield and water productivity outcomes from a field trial that compared three computer simulation models for irrigation scheduling: (1) AquaCrop-OSPy (AQC), (2) the CROPGRO-Cotton module within the DSSAT Cropping System Model (CSM), and (3) the pyfao56 evapotranspiration-based, soil water balance model (FAO). Six irrigation scheduling treatments were established, including the three models used as stand-alone scheduling tools (AQC, CSM, and FAO) and the use of the three models in combination with weekly soil water content (SWC) data from neutron moisture meters (AQCSWC, CSMSWC, and FAOSWC). Two cotton varieties were also evaluated (NexGen 3195 and NexGen 4936). The field trial was conducted during the 2021 and 2022 cotton growing seasons at Maricopa, Arizona. Seasonal irrigation amounts were different among irrigation scheduling treatments (p < 0.05), with 9–21% less water recommended for the AQCSWC, CSMSWC, and FAOSWC treatments as compared to AQC, CSM, and FAO. In 2021, the differences in irrigation amount did not lead to any statistical differences in fiber yield among irrigation treatments, but water productivity for the stand-alone CSM model was significantly reduced compared to the other five irrigation treatments (p < 0.05). In 2022, treatments based on soil water content data reduced yield by 15% as compared to stand-alone model treatments, but the reduction was significant only for FAOSWC. Water productivity differences in 2022 were due to the choice of model rather than the inclusion of soil water content data. In both years, the shorter-season cotton variety (NexGen 3195) yielded greater than the longer-season variety (NexGen 4926), and the former achieved greater water productivity than the latter through the yield improvements (p < 0.05). Taken together, the results suggest that combining soil water content data with irrigation scheduling models was useful for reducing irrigation amounts while often maintaining cotton fiber yield and water productivity; however, issues with weather station aridity and other measurement uncertainties must be addressed to improve the methodology. Keywords: Cotton, Evapotranspiration, Irrigation, Management, Sensor, Simulation, Water.

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Section: Fertilizer Managementmentioning

confidence: 99%

Combining Soil Water Content Data with Computer Simulation Models for Improved Irrigation Scheduling

Thorp

2023

Journal of the ASABE

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“…Following the recommendations of Bronson et al (2021), liquid urea ammonium nitrate (UAN 32-0-0) was uniformly applied in three split applications in both growing seasons, amounting to seasonal nitrogen application rates of 149 kg N ha -1 in 2019 and 136 kg N ha -1 in 2020. A fertigation trailer, which included a fertilizer tank, metering pump, and gasoline-powered generator, was hitched to the lateral-move irrigation machine, and fertilizer was injected into the overhead irrigation pipe.…”

Section: Fertilizer Managementmentioning

confidence: 99%

Agronomic Outcomes of Precision Irrigation Management Technologies with Varying Complexity

Thorp¹,

Calleja²,

Pauli³

et al. 2022

Journal of the ASABE

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HighlightsCotton yield and water productivity were measured for different precision irrigation management solutions.Agronomic improvements from site-specific irrigation based on spatial FAO-56 crop coefficient data were minor.Thermal remote sensing data from unoccupied aircraft systems were able to identify crop water limitations.Integrated sensing and modeling tools that can achieve intended agronomic outcomes should be prioritized.Abstract. Diverse technologies, methodologies, and data sources have been proposed to inform precision irrigation management decisions, and the technological complexity of different solutions is highly variable. Additional field studies are needed to identify solutions that achieve intended agronomic outcomes in simple and cost-effective ways. The objective of this study was to compare cotton yield and water productivity outcomes resulting from different solutions for scheduling and conducting precision irrigation management. A cotton field study was conducted at Maricopa, Arizona, in 2019 and 2020 that evaluated the outcomes of four management solutions with varying technological complexity: (1) a stand-alone evapotranspiration-based soil water balance model with field-average soil parameters (MDL), (2) using site-specific soil data to spatialize the modeling framework (SOL), (3) driving the model with spatial crop coefficients estimated from an unoccupied aircraft system (UAS), and (4) using commercial variable-rate irrigation technology for site-specific irrigation applications (VRI). Soil water content data and thermal UAS data were also collected but used only in post hoc data analysis. Applied irrigation, cotton fiber yield, and water productivity were statistically identical for MDL and SOL. As compared to MDL, the UAS crop coefficient approach significantly reduced applied irrigation by 7% and 14% but also reduced yield by 5% and 26% in 2019 and 2020, respectively (p = 0.05). In 2019 only, the VRI approach maintained yield while significantly reducing applied irrigation by 8% compared to MDL, and water productivity was significantly increased from 0.200 to 0.211 kg m-3 when one outlier datum was removed (p = 0.05). Post hoc data analysis showed that crop water stress information, particularly from UAS thermal imaging data, would likely benefit the irrigation scheduling protocol. Efforts to develop integrated sensing and modeling tools that can guide precision irrigation management to achieve intended agronomic outcomes should be prioritized and will be relevant whether irrigation applications are site-specific or uniform. Keywords: Cotton, Crop coefficient, Drone, FAO-56, Irrigation scheduling, Remote sensing, Site-specific irrigation, Soil mapping, Unoccupied aircraft system, Variable-rate irrigation, Water stress.

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“…Since the measurements are solely based on the changes of pigments in leaves, fluorescence sensing is less susceptible to erroneous signals from bare soil; hence, it is more related to N concentration than reflectance-based parameters, especially at early growth stages before canopy closure or in wide-row crops [14,16,20]. Thus, many researchers have explored and verified the ability of Multiplex to estimate PNC [22][23][24], plant N uptake (PNU), and N nutrition index (NNI) [18,25], and explored the feasibility for in-season N recommendation [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Canopy Fluorescence Sensing for In-Season Maize Nitrogen Status Diagnosis

et al. 2021

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Accurate assessment of crop nitrogen (N) status and understanding the N demand are considered essential in precision N management. Chlorophyll fluorescence is unsusceptible to confounding signals from underlying bare soil and is closely related to plant photosynthetic activity. Therefore, fluorescence sensing is considered a promising technology for monitoring crop N status, even at an early growth stage. The objectives of this study were to evaluate the potential of using Multiplex® 3, a proximal canopy fluorescence sensor, to detect N status variability and to quantitatively estimate N status indicators at four key growth stages of maize. The sensor measurements were performed at different growth stages, and three different regression methods were compared to estimate plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). The results indicated that the induced differences in maize plant N status were detectable as early as the V6 growth stage. The first method based on simple regression (SR) and the Multiplex sensor indices normalized by growing degree days (GDD) or N sufficiency index (NSI) achieved acceptable estimation accuracy (R2 = 0.73–0.87), showing a good potential of canopy fluorescence sensing for N status estimation. The second method using multiple linear regression (MLR), fluorescence indices and GDDs had the lowest modeling accuracy (R2 = 0.46–0.79). The third tested method used a non-linear regression approach in the form of random forest regression (RFR) based on multiple sensor indices and GDDs. This approach achieved the best estimation accuracy (R2 = 0.84–0.93) and the most accurate diagnostic result.

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Optimal internal nitrogen use efficiency for irrigated cotton in the southwestern United States

Cited by 7 publications

References 29 publications

Combining Soil Water Content Data with Computer Simulation Models for Improved Irrigation Scheduling

Combining Soil Water Content Data with Computer Simulation Models for Improved Irrigation Scheduling

Agronomic Outcomes of Precision Irrigation Management Technologies with Varying Complexity

Canopy Fluorescence Sensing for In-Season Maize Nitrogen Status Diagnosis

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