Analysis of interval-grouped data in weed science: The binnednp Rcpp package.

Weed scientists are usually interested in the study of the distribution and density functions of the random variable that relates weed emergence with environmental indices like the hydrothermal time (HTT). However, in many situations, experimental data are presented in a grouped way and, therefore, the standard nonparametric kernel estimators cannot be computed.Kernel estimators for the density and distribution functions for interval-grouped data, as well as bootstrap confidence bands for these functions, have been proposed and implemented in the binnednp package. Analysis with different treatments can also be performed using a bootstrap approach and a Cramér-von Mises type distance. Several bandwidth selection procedures were also implemented. This package also allows to estimate different emergence indices that measure the shape of the data distribution. The values of these indices are useful for the selection of the soil depth at which HTT should be measured which, in turn, would maximize the predictive power of the proposed methods.This paper presents the functions of the package and provides an example using an emergence data set of Avena sterilis (wild oat).The binnednp package provides investigators with a unique set of tools allowing the weed science research community to analyze interval-grouped data.

Spatio-temporal dynamics of maize yield water constraints under climate change in Spain.

Many studies have analyzed the impact of climate change on crop productivity, but comparing the performance of water management systems has rarely been explored. Because water supply and crop demand in agro-systems may be affected by global climate change in shaping the spatial patterns of agricultural production, we should evaluate how and where irrigation practices are effective in mitigating climate change effects. Here we have constructed simple, general models, based on biological mechanisms and a theoretical framework, which could be useful in explaining and predicting crop productivity dynamics. We have studied maize in irrigated and rain-fed systems at a provincial scale, from 1996 to 2009 in Spain, one of the most prominent "hot-spots" in future climate change projections. Our new approach allowed us to: (1) evaluate new structural properties such as the stability of crop yield dynamics, (2) detect nonlinear responses to climate change (thresholds and discontinuities), challenging the usual linear way of thinking, and (3) examine spatial patterns of yield losses due to water constraints and identify clusters of provinces that have been negatively affected by warming. We have reduced the uncertainty associated with climate change impacts on maize productivity by improving the understanding of the relative contributions of individual factors and providing a better spatial comprehension of the key processes. We have identified water stress and water management systems as being key causes of the yield gap, and detected vulnerable regions where efforts in research and policy should be prioritized in order to increase maize productivity.

Controlling annual weeds in cereals by deploying crop rotation at the landscape scale: Avena sterilis as an example.

Weed control through crop rotation has mainly been studied in a nonspatial context. However, weed seeds are often spread beyond the crop field by a variety of vectors. For weed control to be successful, weed management should thus be evaluated at the landscape level. In this paper we assess how seed dispersal affects the interactions between crop rotation and landscape heterogeneity schemes with regard to weed control. A spatially explicit landscape model was developed to study both short- and long-term weed population dynamics under different management scenarios. We allowed for both two- and three-crop species rotations and three levels of between-field weed seed dispersal. All rotation scenarios and seed dispersal fractions were analyzed for both completely homogeneous landscapes and heterogeneous landscapes in which more than one crop was present. The potential of implementing new weed control methods was also analyzed. The model results suggest that, like crop rotation at the field level, crop rotation implemented at the landscape level has great potential to control weeds, whereby both the number of crop species and the cropping sequence within the crop rotation have significant effects on both the short- and long-term weed population densities. In the absence of seed dispersal, weed populations became extinct when the fraction of each crop in the landscape was randomized. In general, weed seed densities increased in landscapes with increasing similarity in crop proportions, but in these landscapes the level of seed dispersal affected which three-crop species rotation sequence was most efficient at controlling the weed densities. We show that ignoring seed dispersal between fields might lead to the selection of suboptimal tactics and that homogeneous crop field patches that follow a specific crop rotation sequence might be the most sustainable method of weed control. Effective weed control through crop rotation thus requires coordination between farmers with regard to cropping sequences, crop allocation across the landscape, and/ or the fraction of each crop across the landscape.

Climate effects and feedback structure determining weed population dynamics in a long-term experiment.

Pest control is one of the areas in which population dynamic theory has been successfully applied to solve practical problems. However, the links between population dynamic theory and model construction have been less emphasized in the management and control of weed populations. Most management models of weed population dynamics have emphasized the role of the endogenous process, but the role of exogenous variables such as climate have been ignored in the study of weed populations and their management. Here, we use long-term data (22 years) on two annual weed species from a locality in Central Spain to determine the importance of endogenous and exogenous processes (local and large-scale climate factors). Our modeling study determined two different feedback structures and climate effects in the two weed species analyzed. While Descurainia sophia exhibited a second-order feedback and low climate influence, Veronica hederifolia was characterized by a first-order feedback structure and important effects from temperature and rainfall. Our results strongly suggest the importance of theoretical population dynamics in understanding plant population systems. Moreover, the use of this approach, discerning between the effect of exogenous and endogenous factors, can be fundamental to applying weed management practices in agricultural systems and to controlling invasive weedy species. This is a radical change from most approaches currently used to guide weed and invasive weedy species managements.