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The use of predictive yield maps is an important tool for the delineation of within-field management zones. In particular, appropriate placement in the field of organically grown produce, as is the case for the processing tomato crop in this study, will ensure higher crop yield. Accurate estimation of yield can be used to plan the best time for harvesting and transport for industrial processing. Apparent electrical conductivity (ECa) and vegetation indices based on crop reflectance are two tools that can be used to help attain these objectives. Developments in the use of sensors have enabled massive georeferenced data sampling of these parameters. The aim of this article is to assess the ECa and normalized difference vegetation index (NDVI) using geostatistical techniques to optimize their use. Principal component analysis was used to evaluate the predictive yield maps developed. ECa was a reasonably good indicator of crop production potential throughout the plot as a whole, but NDVI was the best indicator, offering a better resolution than ECa and a reasonable estimation of yield distribution over the extensive tested crop surface area. [ABSTRACT FROM AUTHOR]

The recent availability of high spatial and temporal resolution satellite imagery has widened its applications in agriculture. Plant breeding and genetics programs are currently adopting unmanned aerial vehicle (UAV) based imagery data as a complement to ground data collection. With breeding trials across multiple geographic locations, UAV imaging is not always convenient. Hence, we anticipate that, similar to UAV imaging, phenotyping of individual test plots from highresolution satellite imagery may also provide value to plant genetics and breeding programs. In this study, high spatial resolution satellite imagery (~38 to 48 cm pixel-1) was compared to imagery acquired using a UAV for its ability to phenotype maize grown in two-row and six-row breeding plots. Statistics (mean, median, sum) of color (red, green, blue), nearinfrared, and vegetation indices such as normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and soil adjusted vegetation index (SAVI) were extracted from imagery from both sources (UAV and satellite) for comparison at three time points. In general, a strong correlation between satellite and UAV imagery extracted NDVI, GNDVI, and SAVI features (especially with mean and median statistics, p < 0.001) was observed at different time points. The correlation of both UAV and satellite image features with yield potential was maximum (p < 0.001) at the third time point (milk/dough growth stages). For example, Pearson's correlation coefficients between mean NDVI, GNDVI, and SAVI features with yield potential were 0.52, 0.54, and 0.51 for data derived from UAV imagery, and 0.34, 0.41, and 0.40 for data derived from satellite imagery, respectively. Machine learning algorithms, including least absolute shrinkage and selection operator (Lasso) regression, were evaluated for yield prediction using vegetation index features that were significantly correlated with observed yield. The relationship between satellite imagery with crop performance can be a function of plot size in addition to crop variability. Nevertheless, with the ongoing improvement of satellite technologies, there is a possibility for the integration of satellite data into breeding programs, thus improving phenotyping efficiencies. [ABSTRACT FROM AUTHOR]

A method widely used for irrigation management determines crop evapotranspiration (ETc) from reference evapotranspiration (ETo) calculations and estimated crop coefficients. However, standard time-based crop coefficients may fail to represent the actual crop water use, for example, when deviations in weather or agronomic constraints appreciably change crop development patterns from typical conditions. In this study, the FAO-56 dual crop coefficient procedures were applied during experiments with wheat to calculate the estimated ETc for irrigation scheduling. The objective of this research was to determine whether basal crop coefficients (Kcb) determined from a normalized difference vegetation index (NDVI treatment) improve the prediction of ETc over a standard application with a locally developed time-based Kcb curve (FAO treatment). The experiments conducted for two seasons in central Arizona included subtreatments, equally replicated within the NDVI and FAO treatments, of three plant densities (typical, dense, and sparse) and two nitrogen levels (high and low) to provide a range of crop development and water use conditions. The effects of plant density and N level resulted in significant differences in measured seasonal ETc. Large variations that occurred in the observed Kcb and ETc trends between subtreatments were better correlated with the NDVI than the FAO treatment. The mean absolute percent difference for predicted ETc was significantly smaller for NDVI than FAO during both seasons. The treatment difference was 5% for the first season, but 10% for the second season when an unexpected early decline in ETc and Kcb was effectively predicted by the NDVI treatment but not by the FAO treatment. NDVI appears to be a robust approach for Kcb estimation of wheat, able to reliably predict actual ETc for both typical and abnormal water use conditions. [ABSTRACT FROM AUTHOR]

Identifying spatial patterns of soil and plant properties can be an efficient method for site-specific management in areas with homogeneous characteristics (i.e., management zones, MZs). In this study, the use of soil apparent electrical conductivity (ECa) is proposed as the main information source for evaluating the spatial variability of soil and plant properties when using this variability to determine potential MZs. This study was conducted in a commercial hedgerow olive grove. Spatial distribution maps of the main soil properties and normalized difference vegetation index (NDVI) were generated by regression-kriging in which ECa was used as a secondary variable. According to the results obtained by the validation process, all maps were accurate. Soil and plant properties and ECa were subjected to principal component analysis (PCA). Two MZs were determined using a fuzzy cluster classification. The MZ map was validated using data related to soil samples, yield, and NDVI. Establishing different MZs was useful for adapting the irrigation strategies to the soil conditions of the plot, which resulted in increased productivity of the hedgerow olive grove. [ABSTRACT FROM AUTHOR]

Current irrigation scheduling is based on well-established crop coefficient and reference evapotranspiration procedures to estimate daily crop evapotranspiration (ETc). Effective irrigation scheduling and efficient irrigation water use can occur when ETc is calculated with crop coefficients representative of actual crop water use conditions. The objective of this research was to evaluate irrigation scheduling using two approaches to estimate the basal crop coefficient (Kcb) during wheat experiments conducted in 2003-2004 and 2004-2005 at Maricopa, Arizona. Each Kcb approach (main treatment) included six subtreatment combinations (three plant densities and two N managements) imposed to create spatial and temporal variations in water use among experimental plots. The first approach (NDVI treatment) estimated Kcb separately for each plot based On normalized difference vegetation index (NDVI) data obtained by frequent canopy reflectance measurements. The second approach (FAO treatment) estimated Kcb uniformly for all plots based on a Kcb curve developed for standard wheat conditions. The Kcb estimates were incorporated within the FAO-56 dual crop coefficient procedures to calculate daily ETc and root zone soil water depletion (Dr). Plot irrigations were provided when the predicted Dr reached 45% of the available soil water During both wheat experiments, considerable variations in measured soil water depletion were observed for subtreatments due to differences in crop water use rates. For the FAO treatment, mean absolute percent difference (MAPD) for predicted Dr was 27% and 40% for 2003-2004 and 2004-2005, respectively. Prediction of Dr was improved significantly for NDVI for both experiments where treatment MAPD was 17% (2003-2004) and 18% (2004-2005). Although mean irrigation application efficiency for NDVI (89%) and FAO (88%) was similar for 2003-2004, it was significantly higher for NDVI (86%) than FAO (77%) for 2004-2005. Differences for irrigation scheduling resulted in significantly lower seasonal irrigation water use for the NDVI than FAO treatment, 8% (2003-2004) and 13% (2004-2005), but did not result in appreciable treatment differences for seasonal ETc, final grain yield, and crop water use efficiency (yield per unit ETc). Consequently, a primary outcome for both experiments was significantly higher irrigation water use efficiency (yield per unit irrigation water) for NDVI than FAO. Incorporating Kcb estimates based on NDVI within existing crop coefficient algorithms provides an opportunity to improve wheat irrigation scheduling strategies for conserving irrigation water while maintaining grain yield potentials. [ABSTRACT FROM AUTHOR]

In this study, we used visible/near-infrared (Vis/NIR) hyperspectral imaging technology to measure the reflectance and gray-level texture features of grape leaf surfaces, and we predicted water content based on those features for more effective irrigation control. Grape vine specimens were potted in the actual field environment, and five levels of water treatment with two replicates were applied to create controlled water stresses. The reflectance and gray-level co-occurrence matrix (GLCM) texture features were used to develop partial least squares (PLS) models for water content prediction. The results showed that when using both reflectance and GLCM texture features, the results achieved a correlation coefficient (rp), root mean square error of prediction (RMSEP), bias, and residual predictive deviation (RPD) of 0.900, 0.826, -2.213e-04, and 2.084, respectively. The experimental results indicated that using a Vis/NIR hyperspectral imaging system could enable this combined method of reflectance and GLCM texture to predict water content of grape vines and be beneficial for grape vine management. [ABSTRACT FROM AUTHOR]