R. Khosla

Evaluation of two crop canopy sensors for nitrogen variability determination in irrigated maize

Advances in precision agriculture technology have led to the development of ground-based active remote sensors that can determine normalized difference vegetation index (NDVI). Studies have shown that NDVI is highly related to leaf nitrogen (N) content in maize (Zea mays L.). Remotely sensed NDVI can provide valuable information regarding in-field N variability and significant relationships between sensor NDVI and maize grain yield have been reported. While numerous studies have been conducted using active sensors, none have focused on the comparative effectiveness of these sensors in maize under semi-arid irrigated field conditions. Therefore, the objectives of this study were (1) to determine the performance of two active remote sensors by determining each sensor’s NDVI relationship with maize N status and grain yield as driven by different N rates in a semi-arid irrigated environment and, (2) to determine if inclusion of ancillary soil or plant data (soil NO3 concentration, leaf N concentration, SPAD chlorophyll and plant height) would affect these relationships. Results indicated that NDVI readings from both sensors had high r2 values with applied N rate and grain yield at the V12 and V14 maize growth stages. However, no single or multiple regression using soil or plant variables substantially increased the r2 over using NDVI alone. Overall, both sensors performed well in the determination of N variability in irrigated maize at the V12 and V14 growth stages and either sensor could be an important tool to aid precision N management.

QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize

In-season nitrogen (N) management of irrigated maize (Zea mays L.) requires frequent acquisition of plant N status estimates to timely assess the onset of crop N deficiency and its spatial variability within a field. This study compared ground-based Exotech nadir-view sensor data and QuickBird satellite multi-spectral data to evaluate several green waveband vegetation indices to assess the N status of irrigated maize. It also sought to determine if QuickBird multi-spectral imagery could be used to develop plant N status maps as accurately as those produced by ground-based sensor systems. The green normalized difference vegetation index normalized to a reference area (NGNDVI) clustered the data for three clear-day data acquisitions between QuickBird and Exotech data producing slopes and intercepts statistically not different from 1 and 0, respectively, for the individual days as well as for the combined data. Comparisons of NGNDVI and the N Sufficiency Index produced good correlation coefficients that ranged from 0.91 to 0.95 for the V12 and V15 maize growth stages and their combined data. Nitrogen sufficiency maps based on the NGNDVI to indicate N sufficient (≥0.96) or N deficient (<0.96) maize were similar for the two sensor systems. A quantitative assessment of these N sufficiency maps for the V10–V15 crop growth stages ranged from 79 to 83% similarity based on areal agreement and moderate to substantial agreement based on the kappa statistics. Results from our study indicate that QuickBird satellite multi-spectral data can be used to assess irrigated maize N status at the V12 and later growth stages and its variability within a field for in-season N management. The NGNDVI compensated for large off-nadir and changing target azimuth view angles associated with frequent QuickBird acquisitions.

On‐the‐go active remote sensing for efficient crop nitrogen management

Purpose – To describe the function and use of the GreenSeeker™ active remote sensor used to detect crop nitrogen status.Design/methodology/approach – In this paper, the GreenSeeker active remote sensor and its use in irrigated maize production systems will be described. A brief discussion of the science of using remote sensing for studying plants is presented. Additionally, a summary of observations collected from field trials is presented.Findings – The GreenSeeker active sensor has tremendous potential for accurately characterizing crop variability for site‐specific N rate determinations in the Western Great Plains region of the United States.Originality/value – This paper discusses the GreenSeeker active sensor for detecting crop variability. Data from the GreenSeeker can be used to make site‐specific nitrogen fertilizer applications which may lead to improved nitrogen use efficiency.

Spatial Variation and Site-Specific Management Zones

Many approaches have been proposed over the last two decades for managing the spatial variation of soil and crops. In this chapter we discuss the importance of quantifying and managing spatial variation in crop production fields to implement site-specific crop management. We outline the challenges that soil and crop scientists have addressed since the inception of precision agriculture (PA) in terms of managing soil spatial variation, and the development of simple, stable and inexpensive techniques for quantifying and managing it with tools such as site-specific management zones. This chapter summarizes and cites the work of several scientists who have worked in the area of development and evaluation of site-specific management zones from around the world. Geostatistics is being applied increasingly in PA because of the need for accurate maps on which to base site-specific management. For soil and crop properties that require costly sampling and analysis, there are often insufficient data for geostatistical analyses and this chapter shows how management zones can provide an interim solution to more comprehensive site-specific management. Physical and chemical soil properties have been the most widely used properties for delineating management zones, however, intensive data from remote and proximal sensors are being used increasingly. The case study describes methods of delineating and evaluating management zones.

Influence of climatic conditions, topography and soil attributes on the spatial distribution of site productivity index of the species rich forests of Jalisco, Mexico

This paper presents an approach based on field data to model the spatial distribution of the site productivity index (SPI) of the diverse forest types in Jalisco, Mexico and the response in SPI to site and climatic conditions. A linear regression model was constructed to test the hypothesis that site and climate variables can be used to predict the SPI of the major forest types in Jalisco. SPI varied significantly with topography (elevation, aspect and slope), soil attributes (pH, sand and silt), climate (temperature and precipitation zones) and forest type. The most important variable in the model was forest type, which accounted for 35% of the variability in SPI. Temperature and precipitation accounted for 8 to 9% of the variability in SPI while the soil attributes accounted for less than 4% of the variability observed in SPI. No significant differences were detected between the observed and predicted SPI for the individual forest types. The linear regression model was used to develop maps of the spatial variability in predicted SPI for the individual forest types in the state. The spatial site productivity models developed in this study provides a basis for understanding the complex relationship that exists between forest productivity and site and climatic conditions in the state. Findings of this study will assist resource managers in making cost-effective decisions about the management of individual forest types in the state of Jalisco, Mexico.

Temporal and Spatial Stability of Soil Test Parameters Used in Precision Agriculture

Abstract Variable‐rate technology provides crop producers with the opportunity to vary the crop and soil management practices. The objective of this study was to assess the temporal and spatial stability of nitrogen (N), phosphorus (P), potassium (K), zinc (Zn), pH, and soil organic matter (OM) for precision nutrient management. This study was conducted over three growing seasons on a continuous maize (Zea Mays L.) production field in northeastern Colorado, USA. Soil samples were collected using a soil sample grid size of 76.2 m×76.2 m. The field was classified into areas of low, medium, and high productivity potential management zones. Spatial statistical analysis was performed. Measured soil parameters varied significantly over space and time (p<0.01). Management zones were effective in identifying homogenous subregions within the field across time (p<0.01). The data suggest that management zones account for spatial and temporal variability for the various soil parameters evaluated in this study.

EVALUATION OF TWO CROP CANOPY SENSORS FOR NITROGEN RECOMMENDATIONS IN IRRIGATED MAIZE

Crop canopy sensors can provide valuable information about in-field nitrogen (N) variability in maize (Zea mays L.) and can serve as the basis for in-season N recommendations. However, few studies have been conducted to determine how the sensors compare. Therefore, a study was conducted using the two most prominent crop canopy sensors (NTechs GreenSeeker™ red and Holland Scientifics Crop Circle™ amber) to determine if the different sensors recommended different amounts of N at the V12 maize growth stage. Results show that each sensor recommended the same amount of N at the V12 growth stage (N recommendations by sensor were not significantly different). The N algorithms developed for each sensor also calculated unbiased N recommendations suggesting that the methodology of algorithm development was valid as was the estimate of required N at maize growth stage V12. Therefore, both crop canopy sensors performed equally in terms of N recommendations in this study.

Spatial variability of atrazine and metolachlor dissipation on dryland no-tillage crop fields in Colorado.

An area of interest in precision farming is variable-rate application of herbicides to optimize herbicide use efficiency and minimize negative off-site and non-target effects. Site-specific weed management based on field scale management zones derived from soil characteristics known to affect soil-applied herbicide efficacy could alleviate challenges posed by post-emergence precision weed management. Two commonly used soil-applied herbicides in dryland corn (Zea mays L.) production are atrazine and metolachlor. Accelerated dissipation of atrazine has been discovered recently in irrigated corn fields in eastern Colorado. The objectives of this study were (i) to compare the rates of dissipation of atrazine and metolachlor across different soil zones from three dryland no-tillage fields under laboratory incubation conditions and (ii) to determine if rapid dissipation of atrazine and/or metolachlor occurred in dryland soils. Herbicide dissipation was evaluated at time points between 0 and 35 d after soil treatment using a toluene extraction procedure with GC/MS analysis. Differential rates of atrazine and metolachlor dissipation occurred between two soil zones on two of three fields evaluated. Accelerated atrazine dissipation occurred in soil from all fields of this study, with half-lives ranging from 1.8 to 3.2 d in the laboratory. The rapid atrazine dissipation rates were likely attributed to the history of atrazine use on all fields investigated in this study. Metolachlor dissipation was not considered accelerated and exhibited half-lives ranging from 9.0 to 10.7 d in the laboratory.

Interpolation type and data computation of crop yield maps is important for precision crop production

ABSTRACT Technological advances in precision agriculture in the last two decades have made yield monitoring and mapping an economically feasible option or practice for farmers. Differentially corrected Global Positioning System (GPS)-equipped yield monitoring system on a combine allows collection of georeferenced yield data which when coupled with a geographic information system (GIS) can generate yield maps via several interpolation techniques. Scientists and practitioners have reported to use multiple different types of interpolation techniques to process yield data. However, one of the aspects that still need to be elucidated is the influence of the different interpolation methods on the quality of the resulting thematic yield maps. The objective of this study was to investigate the influence of three interpolation methods (i.e., inverse of distance, inverse of square distance, and ordinary kriging) commonly used in developing yield maps. An index for the comparison of errors (ICE) was proposed to provide an objective criterion for selecting an experimental variogram model to use with the kriging. Results indicate that inverse distance squared performed slightly better in predicting yields than either inverse distance or ordinary kriging. With a mean absolute difference varying from 0.04 to 0.32 t ha−1 corresponding to a relative deviation from 1.20 to 7.53%, the management decisions can differ in some cases based on the type of interpolation implemented.

Comparison between pixel- and object-based image classification of a tropical landscape using Système Pour l’Observation de la Terre-5 imagery

Abstract Based on the Système Pour l’Observation de la Terre-5 imagery, two main techniques of classifying land-use categories in a tropical landscape are compared using two supervised algorithms: maximum likelihood classifier (MLC) and K -nearest neighbor object-based classifier. Nine combinations of scale level (SL10, SL30, and SL50) and the nearest neighbor (NN3, NN5, and NN7) are investigated in the object-based classification. Accuracy assessment is performed using two main disagreement components, i.e., quantity disagreement and allocation disagreement. The MLC results in a higher total disagreement in total landscape as compared with object-based image classification. The SL30-NN5 object-based classifier reduces allocation error by 250% as compared with the MLC. Therefore, this classifier shows a higher performance in land-use classification of the Langat basin.