a b s t r a c tSite-specific management demands the identification of subfield regions with homogeneous characteristics (management zones). However, determining subfield areas is difficult because of complex correlations and the spatial variability of soil properties and nutrient concentrations, responsible for variations in crop yields within the field. We evaluated whether apparent electrical conductivity (EC a ) is a potential estimator of soil properties and nutrients, and a tool for the delimitation of homogeneous zones. Two field sites with several soil series were studied in southeastern Cordoba Province, Argentina. Soil properties and nutrient concentrations were compared with EC a using principal components (PC)-stepwise regression and ANOVA. The PC-stepwise regression showed that soil properties (pH, EC 1:2.5 , CEC, SOM) and nutrients (Na +2 , Mg +2 , Mn +2 , Cu +2 , Ca +2 , Zn +2 , Fe +2 ) are key loading factors to explain the EC a (R 2 > 0.90). In contrast, K + , P, NO À 3 -N and SO À2 4 -S), content were not able to explain the EC a . The ANOVA showed that EC a measurements successfully delimited two homogeneous soil zones associated with the spatial distribution of soil properties and some nutrients (Na +2 , Mg +2 , Mn +2 , Cu +2 , Ca +2 , Zn +2 , Fe +2 ). These results suggest that field-scale EC a maps have the potential to design sampling zones to implement site-specific management strategies.

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Corn (Zea mays L.) is one of the most sensitive crops to planting pattern and early-season uniformity. The most common method to determine number of plants is by visual inspection on the ground but this field activity becomes time-consuming, labor-intensive, biased, and may lead to less profitable decisions by farmers. The objective of this study was to develop a reliable, timely, and unbiased method for counting corn plants based on ultra-high-resolution imagery acquired from unmanned aerial systems (UAS) to automatically scout fields and applied to real field conditions. A ground sampling distance of 2.4 mm was targeted to extract information at a plant-level basis. First, an excess greenness (ExG) index was used to individualized green pixels from the background, then rows and inter-row contours were identified and extracted. A scalable training procedure was implemented using geometric descriptors as inputs of the classifier. Second, a decision tree was implemented and tested using two training modes in each site to expose the workflow to different ground conditions at the time of the aerial data acquisition. Differences in performance were due to training modes and spatial resolutions in the two sites. For an object classification task, an overall accuracy of 0.96, based on the proportion of corrected assessment of corn and non-corn objects, was obtained for local (per-site) classification, and an accuracy of 0.93 was obtained for the combined training modes. For successful model implementation, plants should have between two to three leaves when images are collected (avoiding overlapping between plants). Best workflow performance was reached at 2.4 mm resolution corresponding to 10 m of altitude (lower altitude); higher altitudes were gradually penalized. The latter was coincident with the larger number of detected green objects in the images and the effectiveness of geometry as descriptor for corn plant detection.

A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at mid-growing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington) of Kansas (total of 457 ha). Three basic tests were conducted on the data: (1) spatial dependence on each of the yield and vegetation indices (VIs) using Moran's I test; (2) model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS) and spatial econometric (SPL) models; and (3) model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran's I test) for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG) was tested positive and statistically significant for most of the fields (p < 0.05), except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02) was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to inform precision agricultural management decisions.

Peralta, N. R., Costa, J. L., Balzarini, M. and Angelini, H. 2013. Delineation of management zones with measurements of soil apparent electrical conductivity in the southeastern pampas. Can. J. Soil Sci. 93: 205–218. Site-specific management demands the identification of subfield regions with homogeneous characteristics (management zones). However, determination of subfield areas is difficult because of complex correlations and spatial variability of soil properties responsible for variations in crop yields within the field. We evaluated whether apparent electrical conductivity (ECa) is a potential estimator of soil properties, and a tool for the delimitation of homogeneous zones. ECa mapping of a total of 647 ha was performed in four sites of Argentinean pampas, with two fields per site composed of several soil series. Soil properties and ECa were analyzed using principal components (PC)–stepwise regression and ANOVA. The PC–stepwise regression showed that clay, soil organic matter (SOM), cation exchange capacity (CEC) and soil gravimetric water content (θg) are key loading factors, for explaining the ECa (R 2≥0.50). In contrast, silt, sand, extract electrical conductivity (ECext), pH values and [Formula: see text]-N content were not able to explain the ECa. The ANOVA showed that ECa measurements successfully delimited three homogeneous soil zones associated with spatial distribution of clay, soil moisture, CEC, SOM content and pH. These results suggest that field-scale ECa maps have the potential to design sampling zones to implement site-specific management strategies.

, "Spatio-temporal evaluation of plant height in corn via unmanned aerial systems," J. Appl. Remote Sens. 11(3), 036013 (2017), doi: 10.1117/1.JRS.11.036013. Abstract. Detailed spatial and temporal data on plant growth are critical to guide crop management. Conventional methods to determine field plant traits are intensive, time-consuming, expensive, and limited to small areas. The objective of this study was to examine the integration of data collected via unmanned aerial systems (UAS) at critical corn (Zea mays L.) developmental stages for plant height and its relation to plant biomass. The main steps followed in this research were (1) workflow development for an ultrahigh resolution crop surface model (CSM) with the goal of determining plant height (CSM-estimated plant height) using data gathered from the UAS missions; (2) validation of CSM-estimated plant height with ground-truthing plant height (measured plant height); and (3) final estimation of plant biomass via integration of CSM-estimated plant height with ground-truthing stem diameter data. Results indicated a correlation between CSM-estimated plant height and ground-truthing plant height data at two weeks prior to flowering and at flowering stage, but high predictability at the later growth stage. Log-log analysis on the temporal data confirmed that these relationships are stable, presenting equal slopes for both crop stages evaluated. Concluding, data collected from low-altitude and with a low-cost sensor could be useful in estimating plant height. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.