High guayule rubber production with subsurface drip irrigation in the US desert Southwest

Guayule is being produced for natural rubber in US desert areas, where irrigation requirements are high. Improved irrigation practices and methods are required to increase guayule yields and reduce its water use. Presently, there is no information available on guayule produced using subsurface drip irrigation (SDI). Therefore, we conducted an SDI guayule field study in 2012-2015 in Maricopa, Arizona, US. The objectives were to evaluate guayule dry biomass (DB), rubber yield (RY), and crop evapotranspiration (ETc) responses to water application level, and to compare these results to previously reported guayule irrigation studies. Guayule seedlings were transplanted in the field in October 2012 at 0.35-m spacing, in 100-m long rows, spaced 1.02 m apart. The field had 15, 8-row wide plots (5 irrigation treatments x 3 replicates). Irrigation treatments were imposed in a randomized complete block design starting in May 2013. Irrigation scheduling was based on the measured soil water depletion percentage (SWDp) of a fully-irrigated treatment, defined as 100% ETc replacement, and maintained at similar or equal to 20-35% SWDp . The other treatments received 25%, 50%, 75%, and 125% of irrigation applied to the 100% treatment on each day of irrigation. Destructive samples for dry biomass, rubber, and resin contents were periodically taken from each plot between February and November of each year until the guayule was bulk-harvested in March 2015. Results indicated ETc, DB, and RY increased with total water applied (irrigation + rain), which varied between treatments from 2080 to 4900 mm for the 29-month growing season. Final dry biomass and rubber yields of 61.2 Mg/ha and 3430 kg/ha, respectively, were achieved with the highest irrigation treatment level (125%) and these yields were significantly higher than those under all other irrigation levels. All SDI irrigation treatments except for the lowest 25% level had rubber yields from 24 to 200% greater than the maximum RY achieved under a companion surface irrigation study conducted simultaneously in Maricopa.Biomass Research and Development Initiative Competitive Grant [2012-10006-19391 OH]24 month embargo; published online: 24 April 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models

Remote sensing technology can rapidly provide spatial information on crop growth status, which ideally could be used to invert radiative transfer models or ecophysiological models for estimating a variety of crop biophysical properties. However, the outcome of the model inversion procedure will be influenced by the timing and availability of remote sensing data, the spectral resolution of the data, the types of models implemented, and the choice of parameters to adjust. Our objective was to investigate these issues by inverting linked radiative transfer and ecophysiological models to estimate leaf area index (LAI), canopy weight, plant nitrogen content, and yield for a durum wheat (Triticum durum) study conducted in central Arizona over the winter of 2010–2011. Observations of crop canopy spectral reflectance between 268 and 1095 nm were obtained weekly using a GER 1500 spectroradiometer. Other field measurements were regularly collected to describe plant growth characteristics and plant nitrogen content. Linkages were developed between the DSSAT Cropping System Model (CSM) and the PROSAIL radiative transfer model (CSM-PROSAIL) and between the DSSAT-CSM and an empirical model relating NDVI to LAI (CSM-Choudhury). The PEST parameter estimation algorithm was implemented to adjust the leaf area growth parameters of the CSM by minimizing error between measured and simulated NDVI or canopy spectral reflectance. A genetic algorithm was implemented to identify the optimum combination of remote sensing observations required to optimize simulations of LAI through model inversion. The relative root mean squared error (RRMSE) between measured and simulated LAI was 24.1% for the CSM-PROSAIL model, whereas the stand-alone PROSAIL and CSM models simulated LAI with RRMSEs of 40.7% and 27.8%, respectively. Wheat yield was simulated with RRMSEs of 12.8% and 10.0% for the lone CSM model and the CSM-PROSAIL model, respectively. Optimized leaf area growth parameters for CSM-PROSAIL were different among cultivars (p\u3c0.05), while those for CSM-Choudhury were not. Only two observations, one at mid-vegetative growth and one at maximum vegetative growth, were required to optimize LAI simulations for CSM-PROSAIL, whereas CSM-Choudhury required four observations. Inverting CSM-PROSAIL using hyperspectral data offered several advantages as compared to the CSM-Choudhury inversion using a simple vegetation index, including better estimates of crop biophysical properties, different leaf area growth parameter estimates among cultivars (p\u3c0.05), and fewer required remote sensing observations for optimum LAI simulation

Long-term Effects of Mineral Versus Organic Fertilizers on Activity and Structure of the Methanotrophic Community in Agricultural Soils

Agricultural practices, such as mineral nitrogen fertilization, have an impact on the soil's ability to oxidize methane, but little is known about the shifts in the methanotrophic community composition associated with these practices. Therefore, the long-term effect of both mineral (NH4NO3) and organic (manure and GFT-compost) fertilizer applications on the soil methanotrophic community activity and structure were investigated. Both high and low affinity methane oxidation rates were lower in the soil treated with mineral fertilizer compared to the other soils. An enhanced nitrate concentration was observed in the mineral fertilized soil but nitrate did not show a direct affect on the high affinity methane oxidation. In contrast, the low affinity methane oxidation was slowed down by increased nitrate concentrations, which suggests a direct effect of nitrate on low affinity methane oxidation. Denaturing gradient gel electrophoresis (DGGE) analysis of 16S rRNA gene fragments specific for methanotrophs revealed a distinct community between the mineral and organic fertilized soils as extra Type I methanotrophic bands (phylotypes) became visible in the organic fertilized soils. These phylotypes were not visible in the patterns of the added organic fertilizers suggesting an indirect effect of the organic fertilizers on the methanotrophic community. Additionally, a molecular analysis was performed after the low affinity methane oxidation test. The enhanced methane concentrations used in the test enriched certain low affinity methanotrophs in the organic fertilized soils but not in the mineral fertilized soil. Supporting the molecular and functional observations, fatty acids characteristic for methanotrophs were less abundant in the soil treated with mineral fertilizer compared to the soil treated with compost. In conclusion, the function and molecular and chemical composition of the methanotrophic community are all altered in soil fertilized with mineral fertilizer