Kevin F. Bronson

Relationship between cotton yield and soil electrical conductivity, topography, and Landsat imagery

Understanding spatial and temporal variability in crop yield is a prerequisite to implementing site-specific management of crop inputs. Apparent soil electrical conductivity (ECa), soil brightness, and topography are easily obtained data that can explain yield variability. The objectives of this study were to evaluate the spatial and temporal variability in cotton (Gossypium hirsutum L.) yield and determine the relationship between yield and soil ECa, topography, and bare soil brightness at a field level in multiple growing seasons. A 50-ha field grown with cotton from 2000 to 2003 and 2005 on the Southern High Plains of Texas was selected for this study. Yield was negatively correlated with bare soil brightness (−0.47xa0<xa0rxa0<xa0−0.33 for red band) and positively correlated with ECa (0.08xa0<xa0rxa0<xa00.29 for 30-cm ECa and 0.28xa0<xa0rxa0<xa00.44 for 90-cm ECa). Yield had stronger correlation with relative elevation and slope than with profile curvature and planar curvature. Combined, ECa, topographic attributes, and bare soil brightness explained up to 70.1xa0% of cotton yield variability. Bare soil brightness and ECa were strongly related to soil texture. Brighter soils with low ECa values had lower clay content. Yield and soil properties had stronger correlation in dry growing seasons than in wet growing seasons. Cotton yield variability pattern was relatively stable across different growing seasons. Soil texture was one of the greatest factors influencing cotton yield variability. Results of this study provide a basis for site-specific management of yield goals and variable rate application of water, fertilizers, seeds, and other inputs.

A model-independent open-source geospatial tool for managing point-based environmental model simulations at multiple spatial locations

A novel geospatial tool box named Geospatial Simulation (GeoSim) has been developed, which can be used to manage point-based model simulations at multiple locations using geospatial data within a geographic information system (GIS). The objectives of this paper were to describe GeoSim and demonstrate its use. GeoSim has been developed as a plug-in for Quantum GIS, and both of these software programs are open-source and freely available. An important feature of GeoSim is its model-independent nature, meaning any point-based simulation model that uses ASCII files for input and output can be managed spatially. GeoSim facilitates the transfer of geospatial data from the GIS database to the model input files and from the model output files back to the GIS database. GeoSim presently includes six software tools, each with a graphical user interface. A case study demonstrates the use of GeoSim for processing geospatial data layers at a field site, conducting spatial model simulations, and optimizing model parameters for site-specific conditions. Two cropping system models, AquaCrop and the DSSAT Cropping System Model, were implemented to simulate seed cotton yield in response to irrigation management, nitrogen management, and soil texture variability for a 14 ha study area near Lamesa, Texas. Geoprocessing tools within GeoSim were able to summarize 5592 data points within 405 polygon features in 3.8 s. Simulation tools were able to swap 33,316 and 44,550 parameters values to complete 405 spatial simulations with the AquaCrop and DSSAT models in 112 s and 398 s, respectively. These results demonstrate the utility of GeoSim for summarizing large geospatial data sets and transferring the data to the file formats of multiple models. Simulation duration was increased as compared to stand-alone model simulations without parameter swapping, which may be problematic for applications requiring large numbers of simulations. The flexible design of GeoSim is intended to support spatial modeling exercises for a variety of models and environmental applications.

Integrating geospatial data and cropping system simulation within a geographic information system to analyze spatial seed cotton yield, water use, and irrigation requirements

The development of sensors that provide geospatial information on crop and soil conditions has been a primary success for precision agriculture. However, further developments are needed to integrate geospatial data into computer algorithms that spatially optimize crop production while considering potential environmental impacts and resource limitations. The objective of this research was to combine several information technologies, including remote sensing, a cropping system model, and a geographic information system (GIS), to synthesize and interpret geospatial data collected during two irrigation scheduling experiments conducted in 2009 and 2011 in a 5-ha cotton field in central Arizona. The Geospatial Simulation (GeoSim) plug-in for Quantum GIS was used to manage geospatial data and conduct site-specific simulations with the CSM-CROPGRO-Cotton model. Simulated annealing optimization was used to adjust five model parameters to simulate site-specific conditions in 320 zones across the field. Using input parameters for GeoSim, a multiple criteria objective function was developed to incorporate measured and simulated leaf area index (LAI), crop canopy height, seed cotton yield, and evapotranspiration (ET) for site-specific optimization of CSM-CROPGRO-Cotton. Parameter identifiability and equifinality issues associated with model inversion were investigated. The optimized model was used for post hoc analysis of irrigation rates that maximized site-specific irrigation water use efficiency. With spatial optimization, the model was able to simulate LAI with root mean squared errors (RMSE) of 15 and 8xa0% in the 2009 and 2011 experiments, respectively. The RMSEs between measured and simulated canopy height, seed cotton yield, and ET were 5xa0% or less in both seasons. Some parameters were more identifiable than others during model inversions. Multiple temporal estimates of LAI were effective for constraining the model’s specific leaf area parameter (SLAVR, cm2 g−1), but lack of information on root growth reduce identifiability of a parameter related to that process (SRGF0). Post-hoc simulation analysis of irrigation management options showed that irrigation schedules based on remotely sensed vegetation indices increased irrigation water use efficiency as compared to traditional scheduling methods, particularly in the 2009 growing season. In 2011, the analysis showed that all scheduling methods resulted in excess irrigation application, and higher deep seepage rates were simulated in that season. Taken together, the results demonstrate that well-designed software tools and algorithms for data processing and interpretation can be potentially transformative for integrating multiple geospatial data sets to compute optimum scenarios for precision irrigation management.

Management to Reduce Greenhouse Gas Emissions in Western U.S. Croplands

Agriculture is a major activity in the western U.S. with approximately 57 million ha of harvested cropland of which 27% is irrigated; however, irrigated crops account for a high proportion of the economic returns because of their high economic value. We sought to summarize greenhouse gas (GHG) flux research from crop production systems in the western U.S. published from 2005 to 2011. Limited GHG emissions data were found from irrigated cropping systems in California (grain, rice, vegetable, orchards), Texas (cotton), Colorado (corn), and Washington (corn and potato), and from dryland wheat systems in Montana and North Dakota. Converting from conventional tillage (CT) to minimum-till (MT) or no-till (NT) production generally sequestered soil organic carbon (SOC) and reduced carbon dioxide (CO 2 ) emissions in many cropping systems, but not all. Methane (CH 4 ) flux was not greatly influenced by crop management practices, except in rice and manure production systems. Nitrous oxide (N 2 O) emissions were affected by N availability, climatic factors, irrigation, and crop management practices, and tended to be lower under dryland than irrigated cropping conditions. Reducing N fertilization rate and selecting the right N source can reduce N 2 O emissions as much as 50%. Use of microjet sprinkler or subsurface drip irrigation reduced N 2 O emissions in vineyards and orchards as much as 50% compared to surface drip systems. Available GHG data could be used to verify models and develop local mitigation practices, but due to the large diversity of cropping systems and ecoregions, and a lack of representative cropping system GHG databases, generalized mitigation recommendations for the western U.S. are not possible at this time.

Hyperspectral data mining to identify relevant canopy spectral features for estimating durum wheat growth, nitrogen status, and grain yield

Abstract While hyperspectral sensors describe plant canopy reflectance in greater detail than multispectral sensors, they also suffer from issues with data redundancy and spectral autocorrelation. Data mining techniques that extract relevant spectral features from hyperspectral data will aid the development of novel sensors for plant trait estimation. The objectives of this research were to (1) compare broad-band reflectance, narrow-band reflectance, and spectral derivatives for estimation of durum wheat traits in the field and (2) develop a genetic algorithm to identify the most relevant spectral features for durum wheat trait estimation. Experiments at Maricopa, Arizona during the winters of 2010–2011 and 2011–2012 tested six durum wheat cultivars with six split-applied nitrogen (N) fertilization rates. Durum wheat traits, including leaf area index, canopy dry weight, and plant N content, were measured from destructive biomass samples on four occassions in each growing season. Grain yield and grain N content were also measured. Canopy spectral reflectance data in 701 narrow wavebands from 350xa0nm to 1050xa0nm were collected weekly using a field spectroradiometer. First- and second-order spectral derivatives were calculated using Savitzky-Golay filtering. The narrow-band data were also used to estimate reflectance in broad wavebands, as typically collected by two commercial multispectral instruments. Partial least squares regression (PLSR) compared the ability of each spectral data set to estimate each measured durum wheat trait. A genetic algorithm was developed to mine narrow-band canopy reflectance and spectral derivative data for spectral features that improved estimates of durum wheat traits. Multispectral data in 4 broad bands estimated leaf area index, canopy dry weight, and plant N content with root mean squared errors of cross validation (RMSECV) between 33.0% and 67.6%, while hyperspectral data in 701 narrow bands reduced RMSECV to values between 19.3% and 36.3%. Use of the genetic algorithm to identify less than 25 relevant spectral features further reduced RMSECV to values between 15.1% and 30.7%. Grain yield was optimally estimated from canopy spectral measurements between 110 and 130xa0days after planting with RMSECV less than 7.6% using the genetic algorithm approach. The timing corresponded to anthesis and early grain fill when presence of wheat heads likely affected canopy spectral reflectance. By using a genetic algorithm to mine hyperspectral reflectance and spectral derivative data, durum wheat traits were optimally estimated from a subset of relevant canopy spectral features.

Nitrogen Management Affects Nitrous Oxide Emissions under Varying Cotton Irrigation Systems in the Desert Southwest, USA

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 <0.1% with subsurface drip irrigation. The reduction of NO emissions due to addition of Agrotain Plus to urea ammonium nitrate was inconsistent. This study provides unique data on NO emissions in arid-land irrigated cotton and illustrates the advantage of subsurface drip irrigation as a low NO source system.

Estimating the relative water content of leaves in a cotton canopy

Remotely sensing plant canopy water status remains a long term goal of remote sensing research. Established approaches to estimating canopy water status — the Crop Water Stress Index, the Water Deficit Index and the Equivalent Water Thickness — involve measurements in the thermal or reflective infrared. Here we report plant water status estimates based upon analysis of polarized visible imagery of a cotton canopy measured by ground Multi-Spectral Polarization Imager (MSPI). Such estimators potentially provide access to the plant hydrological photochemistry that manifests scattering and absorption effects in the visible spectral region.

Response of guayule biomass and rubber yield to variable water inputs using subsurface drip irrigation

Abstract. Abstract. Guayule (Parthenium argentatum) is being produced for natural rubber in Arizona, U.S.A, desert areas, where irrigation requirements are high. Improved irrigation management practices are required to increase guayule yield productivity and reduce its water use. A subsurface drip irrigation (SDI) field experiment was initiated in 2012 in Maricopa, Arizona using a guayule cultivar (Yulex-B). The objective was to increase understanding of guayule biomass and rubber yield response to water application rate and soil water status under SDI. The experiment was conducted in Maricopa Agricultural Center (MAC), Maricopa, Arizona, USA. Guayule seedlings (≈95 day old) were transplanted in the field in October, 2012, at a 0.35-m spacing, along 100-m rows, with row spacing of 1.02 m. The field consisted of 15 plots (5 treatments x 3 replicates), 8 rows each. In the spring of 2013, 5 irrigation treatments were imposed on plots in a randomized complete block design. Irrigation treatment levels were 25%, 50%, 75%, 100% and 125% of irrigation applied to the 100% treatment, based on measured soil water depletion (SWD). The field was irrigated when SWD reached 30-35% for the 100% treatment using soil water content measurements applied in a soil water balance model. Pre-final harvest destructive samples were taken from each plot in between April and November of each year until the guayule was harvested in March 2015. Results indicated increased dry biomass, rubber yield, plant height and percent cover with irrigation water amount, which varied from 860 to 2030 mm annually for treatments. Final biomass and rubber yield of 61.2 Mg/ha and 3430 kg/ha, respectively, was achieved with the highest irrigation treatment level (125%) and these were significantly higher than those under all other irrigation levels.

Using RGB-based vegetation indices for monitoring guayule biomass, moisture content and rubber

Abstract. We investigated the use of a ground based digital camera to monitor plant moisture content, biomass and rubber content of guayule. These parameters are typically assessed using destructive plant sampling and lab analyses, which are labor intensive and time consuming. A method to predict these parameters by camera-based (red-green-blue [or RGB]) vegetation indices could offer an effective and affordable alternative to destructive sampling. RGB indices derived from camera images were related to plant moisture content, rubber content, and plant dry biomass weights using linear relationships. Certain vegetation indices derived from image color values were strongly correlated with percent plant moisture content, including the NGrRI index and the BGR index that had correlation coefficients (r) that ranged from 0.77 to 0.87. The RBNI index and the MRBI had the highest correlations (|r|>0.81) with plant biomass. The MSGrR index had the highest correlation with rubber content (r=0.82), however, the index also had essentially the same correlation as for plant biomass. Results suggest the possibility of utilizing RGB indices developed using data from a digital camera to monitor guayule plant moisture content and to estimate plant biomass. While rubber content did correlate to some indices, the strong multicollinearity of these indices to biomass indicates it may be difficult to separately predict rubber content.