Developing an Empirical Yield-Prediction Model Based on Wheat and Wild Oat (Avena fatua) Density, Nitrogen and Herbicide Rate, and Growing-Season Precipitation

Abstract

To develop a more complete understanding of the ecological factors that regulate crop productivity, we tested the relative predictive power of yield models driven by five predictor variables: wheat and wild oat density, nitrogen and herbicide rate, and growing-season precipitation. Existing data sets were collected and used in a meta-analysis of the ability of at least two predictor variables to explain variations in wheat yield. Yield responses were asymptotic with increasing crop and weed density; however, asymptotic trends were lacking as herbicide and fertilizer levels were increased. Based on the independent field data, the three best-fitting models (in order) from the candidate set of models were a multiple regression equation that included all five predictor variables (R 2 5 0.71), a double-hyperbolic equation including three input predictor variables (R 2 5 0.63), and a nonlinear model including all five predictor variables (R 2 5 0.56). The double-hyperbolic, threepredictor model, which did not include herbicide and fertilizer influence on yield, performed slightly better than the fivevariable nonlinear model including these predictors, illustrating the large amount of variation in wheat yield and the lack of concrete knowledge upon which farmers base their fertilizer and herbicide management decisions, especially when weed infestation causes competition for limited nitrogen and water. It was difficult to elucidate the ecological first principles in the noisy field data and to build effective models based on disjointed data sets, where none of the studies measured all five variables. To address this disparity, we conducted a five-variable full-factorial greenhouse experiment. Based on our fivevariable greenhouse experiment, the best-fitting model was a new nonlinear equation including all five predictor variables and was shown to fit the greenhouse data better than four previously developed agronomic models with an R 2 of 0.66. Development of this mathematical model, through model selection and parameterization with field and greenhouse data, represents the initial step in building a decision support system for site-specific and variable-rate management of herbicide, fertilizer, and crop seeding rate that considers varying levels of available water and weed infestation. Nomenclature: Imazamethabenz; wild oat, Avena fatua L. AVEFA; wheat, Triticum aestivum L.

Efficiency of a reduced herbicide rate for Brachiaria brizantha control in sugarcane

“…The use of mathematical models has also been proposed to estimate the herbicide rate, considering the species, populations, and some environmental factors, such as soil nutrient levels and water availability (Kim et al, 2002, 2006, Wagner et al, 2007. With these models, it is possible to estimate herbicide rates that provide effective weed control, reduce production costs, and especially mitigate the environmental impact caused by these products.…”

Competition between Avena sterilis ssp. sterilis and wheat in South Western Spain

“…Previously, Streigbig et al (1989) and Zanin et al (1993) had used the hyperbola rectangular model whereas Torner et al (1991) used the exponential model. Other models more complex, involving several environmental factors (such as rainfall) or agronomic factors (such as the use of herbicides and fertilizers, the variety or the seeding rate) (Wagner et al, 2007), were not taken into account because of the lack of information on all these factors. Furthermore, given the complexity of factors that can influence in the competition process, our purpose was to use simple models that can be easily understood and applied by farmers.…”

Seedsvsfungi: an enzymatic battle in the soil seedbank

“…Management of annual weeds is a major problem in agriculture, impacting crop quality (Akbar et al, 2011;Gibson et al, 2017), crop yield (Oerke, 2006;Soltani et al, 2016Soltani et al, , 2017, and ultimately economic return on investment (Pimentel et al, 2005; de Lange and van Wilgen, 2010). Many factors influence the effect of weeds on crop yields including weed density, weed species, crop species and soil conditions (Wagner et al, 2007). A density of ten wild oat (Avena fatua L.) plants per m 2 can result in as much as 35% yield loss of wheat (Triticum aestivum L.) (Martin and Field, 1988).…”