Kevin. F. Bronson

Nitrogen (N) recovery by maize (Zea mays L.) in harvested grain is typically only 30–45% of applied fertiliser N, with the remainder susceptible to leaching and denitrification. In New Zealand, nitrate-loss regulation has increased reliance on nutrient budgeting models such as OverseerFM®, which until recently assumed a maximum effective maize rooting depth of 60 cm, potentially under-representing N uptake by deep-rooted crops, e.g., maize. Using 15N labelled fertiliser, maize uptake, timing and partitioning of absorbed N from soil depths of 60, 90, and 120 cm were assessed under New Zealand field conditions. A two year field experiment was conducted on a Waikato allophanic soil, with 15N applied at the V6 growth stage via subsurface injection beneath individual plants. Plant tissues were sampled through to maturityto trace depth specific uptake and redistribution. Recovery of 15N in aboveground biomass mostly occurred post-silking, and was 65-69%. Results show that maize can access soil N from ≥120 cm depth, supporting improved representation of deep rooting and late-season N uptake in nutrient budgeting models.

Nitrogen is the most important nutrient in the production of cotton (Gossypium hirsutum L.). Field studies on N use efficiency usually focus on recovery efficiency of added N fertilizer or agronomic N use efficiency. The internal N use efficiency of N (iNUE; lint yield divided by plant N uptake) is less studied but is a key component of N fertilizer recommendations. The objective of this study was to identify the optimum iNUE for irrigated cotton in West Texas and Arizona. Eighteen site‐years of data from N fertilizer management studies from Lubbock, TX, and Maricopa, AZ, from 2000 to 2018 were used. Lint yields with N‐fertilized plots averaged 1,535 and 1,790 kg ha^–1 for West Texas and Arizona, respectively. Corresponding N uptake averaged 128 and 177 kg N ha^–1 for West Texas and Arizona, reflecting average N fertilizer rates of 91 and 133 kg N ha^–1. An optimum iNUE of 12.3 ± 0.5 kg lint kg plant N^–1 was estimated by a linear‐plateau model of lint yields vs. total N uptake. Optimum iNUE corresponded to 1,800 kg lint ha^–1 yield and 146 ± 5 kg N ha^–1 plant uptake. Nitrogen uptake by cotton plants greater than this was theoretically luxury uptake. Updated data on optimum iNUE can feed directly into improving current N fertilizer recommendations based on pre‐plant soil NO₃ tests. In turn, this can translate to greater profits for cotton producers in the southwestern United States and the reduction in export of N from irrigated cotton fields.

Petiole nitrate‐nitrogen (NO ₃ –N) sampling and testing remains a popular in‐season nitrogen (N) management practice in the western United States for cotton ( Gossypium hirsutum L.). However, the present guidelines used by Arizona are greater than 35 yr old and are in need of updating. The objectives of this study were to relate in‐season petiole NO ₃ levels with lint yields and N deficiencies and to revise the former critical deficiency level guidelines by growth stage. Petioles were sampled between first square and peak bloom in nine site‐years of cotton N management field trials in Maricopa and Safford, AZ, from 2014 to 2018. Irrigation type in Maricopa was overhead sprinkler irrigation (OSI) (2014–2015) and subsurface drip irrigation (SDI) (2016–2018). In Safford (2014–2017), surface irrigation (SI) was used. Petiole NO ₃ in SDI was dramatically lower than with SI or OSI, mostly in the deficient range. Lower lint yields in zero‐N treatments compared with pre‐plant soil NO ₃ test–based reference treatments occurred in eight site‐years (Safford 2016 crop lost to late hailstorm) and were considered N deficient. Critical petiole NO ₃ –N levels from 1984 were revised downward 1 g N kg ⁻¹ because several N‐fertilized treatment means of petiole NO ₃ were in that range and did not exhibit an N rate–related yield depression. Sampling cotton plants for petiole NO ₃ analysis should start within 1 wk of first bloom. Petiole NO ₃ dynamics and critical levels in SDI cotton required further study.

Plant height in field crops like cotton (Gossypium hirsutum L.) have long been of great interest to agronomists and plant breeders. Canopy height sensors can replace laborious point hand sampling and enable site‐specific management. Study objectives were to compare manual plant height measurements with ultrasonic distance sensor measurements in subsurface drip‐irrigated cotton, and to assess the effects of nitrogen and water management. Two Honeywell 943 sensors were used weekly to estimate canopy height in a nitrogen‐ and water‐management study in Maricopa, AZ from 2016 to 2018. Hand measurements of plant height–ultrasonic‐sensed height correlation increased with growth stage with r² of .92 to .99 at mid bloom. Root mean square errors ranged from 0.9 to 4.0 cm. Canopy height was strongly affected by nitrogen and water treatments, starting at first square. This rapid‐response ultrasonic distance sensor is accurate and has great potential as a canopy height measuring instrument in cotton.

Use of active optical sensors (AOS) in nitrogen (N) management of row crops continues to grow. Since the first studies in the mid‐1990s, several commercial AOS are now available. Typically, canopy reflectance in red and near infrared (NIR) bands are used to calculate the normalized difference vegetation index (NDVI). More recently, commercially available AOS include a third, red‐edge band that allows the calculation of additional vegetation indices (VIs). We present two studies of five site‐years of N management studies in Maricopa, AZ, on a Casa Grande sandy loam with cotton (Gossypium hirsutum L.). The 2014–2015 study was conducted under an overhead sprinkler irrigation system (OSI), and the 2016–2018 study was in subsurface drip irrigation (SDI). The study objective was to compare the ability of 12 different VIs to detect N deficiency among N treatments from shortly after emergence to mid‐bloom. In the OSI study, which showed delayed, small N treatment effects, NDRE and the chlorophyll index using amber (CIRE) detected N deficiency in zero‐N and in reduced N‐reflectance‐based plots 7–23 d before other VIs did. With SDI, the choice of VI was less critical as several VIs could distinguish N deficiency in zero‐N and in reflectance plots. The commonly used NDVI red (NDVIR) only detected N deficiency in reflectance plots in one of five site‐years. In conclusion, we recommend the use of AOS with NIR and red‐edge bands and the calculation of NDRE or CIRE to guide AOS‐based in‐season N management of irrigated cotton.

Declining water availability in the American Southwest continues to generate interest in efficient subsurface drip irrigation (SDI) for cotton ( Gossypium hirsutum L.) production. Fertigating urea ammonium nitrate (UAN) at low rates with high frequency is an important advantage of SDI. However, N fertilizer management guidelines specific to SDI cotton are lacking. A 3‐yr study was conducted on a Casa Grande sandy loam soil in Maricopa, AZ, to test a pre‐plant soil profile NO ₃ test algorithm and a canopy reflectance approach to manage in‐season N fertilizer for SDI cotton. Treatments included soil test‐based N management, reflectance‐based N management, and zero‐N at 100% evapotranspiration irrigation replacement. A second irrigation level of 70% evapotranspiration replacement included just the soil test‐based N and zero‐N treatments. The five treatments were replicated three times. Soil test–based N treatments received from 172 to 224 kg N ha ⁻¹ , and reflectance‐based N amounts were 112 to 158 kg N ha ⁻¹ . Nitrogen recovery efficiency (RE) of UAN‐N was high, with 24 fertigations during 6 wk between first square and mid bloom ranging from 58 to 93%. The isotope dilution method estimated similar RE in 2017. Residual post‐harvest soil NO ₃ –N was notable only with 70% irrigation. Lint and seed yields were significantly reduced with the 70% irrigation treatment compared with 100% irrigation. The key result of this study is that reflectance‐based N management saved 17 to 112 kg N ha ⁻¹ without reducing lint yields compared with the soil test–based N treatment.

Core Ideas: A 2‐yr field study in Maricopa, AZ, under an overhead sprinkler irrigation system determined the effects of 10 water levels (irrigation fraction 0.5–1.1) and five N fertilizer rates (38–150 kg N ha⁻¹) on seed yield, seed oil, and N use efficiency. Recovery efficiency of N fertilizer by camelina ranged from 12 to 72% for high to low N fertilizer rates, respectively. Oil content of seed was maximum at 40%, and decreased with N rate but increased with water level. Interest is growing in camelina (Camelina sativa L. Crantz) as a biofuel feedstock. However, there has been little camelina research in irrigated arid systems. A 2‐yr field study in Maricopa, AZ, under an overhead sprinkler irrigation system determined the effects of 10 water levels (irrigation fraction 0.5–1.1) and five N fertilizer rates (38–150 kg N ha⁻¹) on seed yield, seed oil content, and N use efficiency. Cultivar Robinson was planted in December 2012 and 2013. Nitrogen fertilizer (urea ammonium nitrate) was applied in three split applications. Irrigation amounts were from 125 to 380 mm, and in‐season rain was 70 and 50 mm, in 2013 and 2014, respectively. Camelina seed yields were maximum at water level 7 (irrigation fraction 0.93) in 2013 at 1800 kg ha⁻¹. Maximum seed yields were 1600 kg ha⁻¹ at water level 6 (irrigation fraction 0.83) in 2014. These highest seed yields were achieved with 150 kg N ha⁻¹ in both years. Oil content (maximum 41%) decreased with N rate but increased with water level. Seed N increased with N rate but decreased with irrigation level. Recovery efficiency of N fertilizer by camelina ranged from 12 to 72%. The results indicate that good high‐oil camelina yields can be produced in the southwestern United States with 320 to 380 mm irrigation plus rain and N fertilizer rates of 150 kg N ha⁻¹.

Understanding the spatial and temporal variations of infiltration in furrows is essential for the design and management of furrow irrigation systems. A key difficulty in quantifying the process is that infiltration depends on the depth of flow, which varies along a furrow and with time. An additional difficulty is that under many field conditions, a large fraction of the infiltrated water flows through cracks and/or macropores. This study examines the spatial and temporal variability of a furrow-irrigated field and evaluates a proposed semiphysical furrow infiltration model that accounts for flow-depth and macroporosity effects. Parameter estimation techniques were used to determine two parameters of the infiltration model, the hydraulic conductivity and the macroporosity term, in addition to the Manning roughness coefficient. The methodology was tested using published data from 30 furrow irrigation data sets collected in six furrows over five irrigation events. The evaluation revealed substantial variations in the final infiltrated volume among furrows and from one irrigation event to the next. Variability patterns differed markedly for infiltration measured during the advance phase in comparison with infiltration measured during the storage phase of the irrigations. Advance-phase infiltration varied systematically between irrigations for all furrows. Interfurrow inflow rate variability contributed to the variability of the infiltration during the postadvance phase, but not during the advance phase. Thus, cracks and/or macropores were an important contributor to the variability of infiltration during the advance phase. The analysis produced reasonable estimates of hydraulic conductivity relative to values reported in the literature. Hydraulic conductivity and post-advance infiltration volumes exhibited similar patterns of temporal variability. Hydraulic conductivity estimates were statistically correlated to the applied inflow rate. Although the reasons for this correlation are not clear, a possible explanation is that they are the result of systematic differences in the applied inflow rate among furrows. As with the advance-phase infiltration volumes, the estimated macroporosity term exhibited greater variation among irrigation events than among furrows during an event. The estimation procedure produced smaller differences between volume balance computed infiltration volumes and predicted values when using the semiphysical infiltration model than when using an empirical infiltration equation. Overall, the results show that the proposed furrow infiltration model represents the infiltration process adequately and that, at least for the studied data sets, the proposed estimation procedure yields a coherent set of infiltration and hydraulic resistance parameter values.