Spatiotemporal normalized ratio methodology to evaluate the impact of field-scale variable rate application (original) (raw)
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Journal of Hydrology, 2005
The simulations of dynamic, spatially distributed non-linear models are impacted by the degree of spatial and temporal aggregation of their input parameters and variables. This paper deals with the impact of these aggregations on the assessment of irrigation scheme performance by simulating water use and crop yield. The analysis was carried out on a 7000 ha irrigation scheme located in Southern Spain. Four irrigation seasons differing in rainfall patterns were simulated (from 1996/1997 to 1999/2000) with the actual soil parameters and with hypothetical soil parameters representing wider ranges of soil variability. Three spatial aggregation levels were considered: (I) individual parcels (about 800), (II) command areas (83) and (III) the whole irrigation scheme. Equally, five temporal aggregation levels were defined: daily, weekly, monthly, quarterly and annually.
2011
Farm-scale trials are being conducted to assess the benefits of variable rate irrigation (VRI). Three farms have been selected where existing sprinkler irrigation systems have recently been modified to provide variable rate control of each individual sprinkler. Irrigation is being varied according to soil and crop differences, and is also being shut off over exclusion zones, such as drains and raceways, and for farm operations such as pasture renovation. Under each VRI irrigator, soil variability has been quantitatively assessed using a mobile soil mapping system, which consists of an electromagnetic (EM) sensor pulled behind an all-terrain vehicle, with an on-board accurate RTK-GPS, datalogger and field computer. The EM sensor measures soil apparent electrical conductivity (EC), and the resulting soil EC maps were ground-truthed and used to define irrigation management zones. Soil moisture sensors have been installed into each zone to monitor real-time soil moisture status. This in...
Key Performance Indicators for Variable Rate Irrigation Implementation on Variable Soils
2009
Decision support tools for precise irrigation scheduling are required to improve the efficiency of irrigation water use globally. This paper presents a method for mapping soil variability and relating it to soil hydraulic properties so that soil management zones for variable rate irrigation can be defined. A soil water balance is used to schedule hypothetical irrigation events based on (i) one blanket application of water to eliminate plant stress, which inevitably over-waters some zones where variable soils exist (uniform rate irrigation, URI) and compares this to (ii) variable rate irrigation (VRI), where irrigation is tailored to specific soil zone available water holding capacity (AWC) values. The key performance indicators: irrigation water use, drainage water loss, nitrogen leaching, energy use, irrigation water use efficiency (IWUE) and virtual water content are used to compare URI and VRI at three contrasting sites using four years climate data for a dairy pasture and maize crop and two years climate data for a potato crop. Our research found that VRI saved 9 − 19 % irrigation water, with accompanying energy saving. Loss of water by drainage was also reduced by 20 − 29% using VRI, which reduced the risk of nitrogen leaching. Virtual water content of these three primary products further illustrates benefits of VRI and shows that virtual water content of potato production used least water per unit of dry matter production.
Evaluation of variable rate irrigation using a remote-sensing-based model
Agricultural Water Management, 2018
Improvements in soil water balance modeling can be beneficial for optimizing irrigation management to account for spatial variability in soil properties and evapotranspiration (ET). A remote-sensing-based ET and water balance model was tested for irrigation management in an experiment at two University of Nebraska-Lincoln research sites located near Mead and Brule, Nebraska. Both fields included a center pivot equipped with variable rate irrigation (VRI). The study included maize in 2015 and 2016 and soybean in 2016 at Mead, and maize in 2016 at Brule, for a total of 210 plot-years. Four irrigation treatments were applied at Mead, including: VRI based on a remote sensing model (VRI-RS); VRI based on neutron probe soil water content measurement (VRINP); uniform irrigation based on neutron probe measurement; and rainfed. Only the VRI-RS and uniform treatments were applied at Brule. Landsat 7 and 8 imagery were used for model input. In 2015, the remote sensing model included reflectance-based crop coefficients for ET estimation in the water balance. In 2016, a hybrid component of the model was activated, which included energy-balance-modeled ET as an input. Both 2015 and 2016 had above-average digitalcommons.unl.edu
ON-FARM EXPERIMENTATION: APPLICATION OF DIFFERENT ANALYTICAL TECHINIQUES FOR INTERPRETATION
Precision agriculture assumes that variable response to inputs, such as nitrogen, can be predicted. Most methods of prediction use models imported from experimentation elsewhere. However, variable rate technology now makes it feasible to implement on-farm experimentation to determine site-specific responses to various inputs. This paper briefly reviews some principals of experimental design and discusses designs which attempt to model spatial variation versus those that do not. A 2D step and sine wave whole field experiment conducted on a wheat field near Wyalkatchem, Western Australia in 1997 was evaluated by analysis of variance, kriging, and wavelets to address the question of what is the optimal seed and basal fertilizer rate to apply to different parts of a field. Estimated effects of seed and basal fertilizer by analysis of variance were small suggesting that average rates of 50 kg ha-2 and 75 kg ha-1 basal fertilizer would be appropriate over the entire field, although maps produced by kriging methods suggested a differential treatment. Wavelet analysis identified a periodic trend in the yield data at approximately the same wavelengths as those in the treatment plans, but only accounted for 8% of the variance, supporting results from analysis of variance. Although these results were disappointing, onfarm experimentation is identified as a bridge between the need for quick, empirical solutions to benefit growers in the short term and the long term need for process-based understanding of crop growth in a multivariate environment.
Operational Research, 2010
A better method is needed to evaluate the effectiveness of precision agricultural practices on research farm fields. We present a novel methodology for formulating the design of an experiment to evaluate the effectiveness of precision agricultural practices. The method combines a georeferenced treatment structure and a georeferenced design structure to build a mixed model that describes and analyzes the site-specific experiment. One or more layers of georeferenced information (obtained by various remote-sensing systems) describing the topography of the research field and its crop attributes may be included as covariates in the mixed model. The concepts of this approach are illustrated through the use of a hypothetical field. Current limitations are also discussed.
Separating spatial and temporal sources of variation for model testing in precision agriculture
Precision Agriculture, 2007
The application of crop simulation models in precision agriculture research appears to require only the specification of some input parameters and then running the model for each desired location in a field. Reports in the extensive literature on modeling have described independent tests for different cultivars, soil types and weather, and these have been presumed to validate the model performance in general. However, most of these tests have evaluated model performance for simulating mean yields for multiple plots in yield trials or in other large-area studies. Precision agriculture requires models to simulate not only the mean, but also the spatial variation in yield. No consensus has emerged about how to test model performance rigorously, or what level of performance is sufficient. In addition, many measures of goodness of fit between the observed and simulated data (i.e., model performance) depend on the range of variation in the observed data. If, for example, inter-annual and spatial sources of variation are combined in a test, poor performance in one might be masked by good performance in the other. Our objectives are to: (1) examine research aims that are common in precision agriculture, (2) discuss expectations of model performance, and (3) compare several traditional and some alternative measures of model performance. These measures of performance are explained with examples that illustrate their limitations and strengths. The risk of relying on a test that combines spatial and temporal data was shown with data where the overall fit was good (r 2 [ 0.8), but the fit within any year was zero. Information gained using these methods can both guide and help to build confidence in future modeling efforts in precision agriculture.