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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, USA to test a pre-plant soil profile NO3 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 % ET irrigation replacement. A second irrigation level of 70 % ET 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 NO3-N was notable only with 70 % irrigation. Lint and seed yields were significantly reduced with the 70 % irrigation treatment compared to 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 to the soil test-based N treatment.

Controlling for experimental error attributable to field heterogeneity is important in high-throughput phenotyping studies that enable large numbers of genotypes to be evaluated across time and space. In the current study, we compared the efficacy of different experimental designs and spatial models in the analysis of canopy spectral reflectance data collected on upland cotton (Gossypium hirsutum L.). Canopy spectral reflectance, as measured by normalized difference vegetation index (NDVI), was measured at first bloom on three upland cotton performance trials conducted in Florence, SC, during 2014 and 2015. The relative efficiency and estimates of genotype effects were compared among randomized complete block, an α-lattice incomplete block, row–column incomplete block, nearest neighbor adjusted, and spatially correlated error models. The row–column model provided the greatest improvement in the precision of genotype effect estimates compared with the randomized complete block model. Genotype rankings based on NDVI varied substantially between the randomized complete block and alternative models, particularly at 5 and 10% selection intensities. These results suggest that the use of more complex experimental designs and spatial analyses should be routinely considered to minimize experimental error due to field heterogeneity and improve the precision and reliability of traits measured using high-throughput phenotyping systems. These findings also indicate that further research into the effects of field heterogeneity on the relationship between NDVI and lint yield in upland cotton is warranted.

Irrigation of food and fiber crops worldwide continues to increase. Nitrogen (N) from fertilizers is a major source of the potent greenhouse gas nitrous oxide (NO) in irrigated cropping systems. Nitrous oxide emissions data are scarce for crops in the arid western United States. The objective of these studies was to assess the effect of N fertilizer management on NO emissions from furrow-irrigated, overhead sprinkler-irrigated, and subsurface drip-irrigated cotton ( L.) in Maricopa, AZ, on Trix and Casa Grande sandy clay loam soils. Soil test- and canopy-reflectance-based N fertilizer management were compared. In the furrow- and overhead sprinkler-irrigated fields, we also tested the enhanced efficiency N fertilizer additive Agrotain Plus as a NO mitigation tool. Nitrogen fertilizer rates as liquid urea ammonium nitrate ranged from 0 to 233 kg N ha. Two applications of N fertilizer were made with furrow irrigation, three applications under overhead sprinkler irrigation, and 24 fertigations with subsurface drip irrigation. Emissions were measured weekly from May through August with 1-L vented chambers. NO emissions were not agronomically significant, but increased as much as 16-fold following N fertilizer addition compared to zero-N controls. Emission factors ranged from 0.10 to 0.54% of added N fertilizer emitted as NO-N with furrow irrigation, 0.15 to 1.1% with overhead sprinkler irrigation, and

Nitrogen fertilizer use efficiency (NUE) is low in surface-irrigated cotton (Gossypium hirsutum L.), especially when adding N to irrigation water. A NO₃ soil-test algorithm was compared with canopy reflectance-based N management with surface- overhead sprinkler-irrigation in central Arizona. The surface irrigation studies also compared fertigation of N fertilizer with knifing-in of N and the addition of a urease and nitrification inhibitor (Agrotain Plus, Koch Agronomic Services, Wichita, KS) to urea ammonium nitrate (UAN). Cotton lint and seed yields responded positively to N fertilizer in all four site-years. Recovery efficiency (RE) of N at low N fertilizer rates (60 to 76 kg N ha–¹) ranged from 21 to 61% with surface irrigation and from 81 to 97% with overhead sprinkler irrigation. Deep percolation below 1.8 m was 4 to 11% of applied surface irrigations and rain, but was undetectable in the overhead sprinkler. Leaching of NO₃ was apparently the largest N loss pathway in the surface-irrigated system. Fertigating UAN into surface irrigation resulted in similar lint yields and RE as knifing UAN. Use of Agrotain Plus with UAN gave similar yields and RE as using UAN alone. Reflectance-based N management using normalized difference vegetation index-amber (NDVIA) saved 50% of N fertilizer of the full soil-test based dose without a yield reduction in three of four site- years. Nitrogen fertilizer was over-prescribed with the soil-test-based treatment. This may have been due to not accounting for N mineralization, which the reflectance method indirectly measures.

Nitrogen is the main nutrient limiting irrigated cotton (Gossypium hirsutum L.) production in the southwestern United States. Canopy spectral reflectance may assess the need for in-season N in irrigated cotton and guide N fertilizer applications. However, calibration of remote sensing indices such as normalized difference vegetative index (NDVI) to the crop's need for N fertilizer is difficult. Well-fertilized reference strips or plots reference NDVI data in the crop area of interest but can result in rank growth and reduced lint yields. Recently, Oklahoma State University developed a calibration procedure of using multiple, sequential, N rate calibration plots, or a ramp approach for wheat (Triticum aestivum L.) and corn (Zea mays L.). We tested this approach in irrigated cotton fields in Lubbock County, Texas, in 2008 and 2009. The main objective of this research was to test a calibration ramp approach to determining optimum in-season N fertilizer rates in irrigated cotton in West Texas. Near infrared, red, and amber reflectance was measured with active spectroradiometers at 1 m above the canopy. Wide ranges in soil type and irrigation amounts influenced NDVI much more than N fertilizer rate. Normalized difference vegetative index at mid-bloom and at peak bloom were positively related to N fertilizer rate in only one ramp in each year. These two ramp-years also had significant N fertilizer rate response in lint yield. Ramps that did not have mid- or peak bloom NDVI responses to N rate, likewise had no lint yield response to N rate. In both low irrigation- low N input and in high irrigation-high N input farms, in-season NDVI correctly predicted lint yield response to N fertilizer rate.

An outbreak of the sweetpotato whitefly, Bemisia tabaci (Gennadius), biotype B occurred in the Imperial Valley, California in 1991. The insects destroyed melon crops and seriously damaged other vegetables, ornamentals and row crops. As a result of the need for sampling technology, we developed a whitefly trap (named the CC trap) that could be left in the field for extended time periods. We used the traps to monitor populations of B. tabaci adults during year-round samplings from 1996 to 2002 to study variations in the weekly trap catches of the insect. The greatest number of B. tabaci adults was recorded in 1996, followed by a continuing annual decrease in trap catches each year through 2002. The overall decline of B. tabaci is attributed in part to the adoption of an integrated pest management (IPM) program initiated in 1992 and reduced melon hectares from 1996 to 2002. Other factors may also have contributed to the population reductions. Seasonally, B. tabaci trap catches decreased during the late summer and fall concurrent with decreasing minimum tempera- tures that are suggested to be a significant factor affecting seasonal activity and reproduction.

Radiometric data can be useful to determine the impact of field heterogeneity, irrigation, and fertilization on plant water and N use. A 2-yr (1998-1999) study was conducted on the South Texas High Plains to investigate cotton (Gossypium hirsutum L.) spectral and agronomic responses to irrigation and N fertilization and to determine the simple and cross correlation among cotton reflectance, plant growth, N uptake, lint yield, site elevation (SE), and soil water and texture. The treatments were irrigation at 50 and 75% of calculated cotton evapotranspiration (ET) and N rates of 0, 90, and 135 kg ha -1 arranged in an incomplete block of size-2 design. Plant and soil spectral properties were investigated within a wavelength of 447 to 1752 nm. Near-infrared (NIR) reflectance was positively correlated with plant biomass and N uptake. Reflectance in the red and midinfrared band increased with SE. The mixed-model analysis showed that cotton NIR reflectance, normalized difference vegetative index (NDVI), soil water, N uptake, and lint yield were significantly affected by irrigation (P < 0.0012). The N treatment had no effect on spectral parameters, and interaction between irrigation and N fertilizer was significant on NIR reflectance (P < 0.0027). All spectral and agronomic parameters measured were associated with SE. The red and NIR reflectance and NDVI were cross-correlated with soil water, sand, clay, and SE across a distance of 60 to 80 m. Characterization of plant and soil reflectance and their spatial structure can be the basis for variable N application on heterogeneous fields to increase N use efficiency.

In many plants, flowering is conspicuous in the field, but enumerating flowers is labor intensive, especially when flowers need to be counted on a daily basis. Frequent trips into plot areas and the physical contact with the plants can result in mechanical damage to plants, which can affect results. The objectives of this work were to develop methods using color digital images to estimate the numbers of flowers present in a scene captured in a digital image and to do all of the processing in a fully automated mode that would allow the counting of flowers in large numbers of images. Images of lesquerella [Lesquerella fendleri (Gray) Wats.] flowers were made using a color digital camera of field plots during the 1996 to 1997 growing season. An automated system to identify all of the pixels in an image that were flowers and to count the number of flower spots in an image was developed. Processing time for individual images was 3.5 min compared with a minimum of 45 min for manual counts. The automated methods produced results that were highly correlated with the number of flowers in an image as counted by hand. Results of the automated methods accurately tracked the temporal changes in flower number. Multiple counts of the same plants were made by the automated methods without damage to either plants that were counted or the plot. This method has the potential to be used to predict harvest dates from peak flowering, to track the response of flowering to environmental conditions, and to evaluate the effects of cultural practices on flowering.

Documenting crop senescence rates is often difficult because of the need for frequent sampling during periods of rapid change and the subjective nature of human visual observations. The purpose of this study was to determine the feasibility of using images produced by a digital camera to measure the senescence rate of wheat and to compare the results with changes in greenness determined by two established methods. Measurements were made as part of an experiment to determine the effects of elevated CO 2 and limited soil nitrogen on spring wheat (Triticum aestivum L.) at the University of Arizona's Maricopa Agricultural Center, near Phoenix, AZ. Greenness' measurements were made during senescence of the crop with a color digital camera, a hand-held radiometer, and a SPAD chlorophyll meter. The green to red (G/R) for each pixel in an image was calculated and the average G/R computed for cropped images from a digital camera representing 1 m 2 for each treatment and sample date. The normalized difference vegetation index (NDVI) was calculated from the red and near-infrared canopy reflectances measured with a hand held radiometer. A SPAD reading was obtained from randomly selected flag leaves. All three methods of measuring plant greenness showed similar temporal trends. The relationships between G/R with NDVI and SPAD were linear over most of the range of G/R. However, NDVI was more sensitive at low values than G/R. G/R was more sensitive above G/R values of 1.2 than SPAD because the upper limits of SPAD measurements were constrained by the amount of chlorophyll in the leaf, while G/R responded to both chlorophyll concentration in the leaves as well as the number of leaves present. Color digital imaging appears useful for quantifying the senescence of crop canopies. The cost of color digital cameras is expected to decrease and the quality and convenience of use to improve.