Beetles and Meteorological Conditions: A Case Study

The meteorological factors study in the beetle population dynamics, as well as its association with vegetation, is of fundamental importance for understanding the variation that occurs in its population. Thus, it was reported the influence of temperature, humidity, insolation and precipitation on the beetles in general and it was presented a case study that examined the relationship between time and population fluctuation of curculionids in Mata de Cocal and an area used for crop rotation and animal grazing, in the city of Teresina, Brazil, from August 2011 to July 2012. It was verified that beetles populations certain are governed and conditioned by meteorological variables to a greater or lesser extent depending on the characteristics of the community itself and the biotic and abiotic environmental factors of the area where they live: the temperature that changes the its metabolic rate, the insolation and humidity that can affect its fertility and longevity can be cited as examples. From the case presented, It was found that the Curculionidae community has a positive association with precipitation and humidity and a negative association with insolation and temperature, being that in native forests curculionids are not as dependent on meteorological variables as in agricultural fields.

Prediction of Crop Yields under a Changing Climate

The impact of increasing climate variability on crop yield is now evident. Predicting the potential effects of climate change on crops prompts the use of statistical models to measure how the crop responds to climate variables. This chapter examines the usage of regression analysis in predicting crop yield under a changing climate. Data quality control is explained and application of descriptive statistics, correlation analysis and contingency tables discussed. Methodological aspects of crop yield modeling and prediction using climate variables are described. Estimation of yield via a multilinear regression approach is outlined and an overview of statistical model verification introduced. The study recommends the usage of regression models in estimating crop yield in consideration of many other externalities that can contribute to yield change.