Aude Barbottin

Global sensitivity analysis for calculating the contribution of genetic parameters to the variance of crop model prediction

Abstract Dynamic models are often used to predict the effects of farmers’ practices on crop yield, crop quality, and environment. These models usually include many parameters that must be estimated from experimental data before practical use. Parameter estimation is a difficult problem especially when some of the parameters vary across genotypes. These genetic parameters may be estimated from plant breeding experiments but this is very costly and requires a lot of experimental work. Moreover, some of the genetic parameters may account for only a very small part of the output variance and, so, do not deserve an accurate determination. This paper shows how methods of global sensitivity analysis can be used to evaluate the contributions of the genetic parameters to the variance of model prediction. Two methods are applied to a complex crop model for estimating the sensitivity indices associated to 13 genetic parameters. The results show that only five genetic parameters have a significant effect on crop yield and grain quality.

Farmland birds and arable farming, a meta-analysis

Declines in farmland bird populations have been principally attributed to the intensification of agriculture. In response, agri-environmental schemes and organic farming have been introduced with the aim of making farmland better able to support wildlife populations. These “bird-friendly” agricultural practices include using more diverse crop rotations, stopping the use of pesticides, and creating more heterogeneous landscapes and are expected to create more food resources and nesting habitats for birds. Many studies have been published that evaluate the success or failure of agricultural practices to increase bird abundance. While many studies have found that most organic farming practices are beneficial to birds, other studies have found that some organic farming practices, such as using increased tillage passes, are not beneficial to birds. We conducted a search of the literature and used a meta-analysis approach to analyze the relationship between farming practices and bird populations. We first tested whether organic agriculture is more favorable to farmland birds of Europe and North America compared to conventional agriculture. We used data from 16 experiments and six publications that fulfilled fixed criteria for inclusion in the meta-analysis. We found that organic agriculture had a global positive effect on bird abundance compared to conventional agriculture. However, this effect was significant in only five out of the 16 site-year combinations tested. We also found that the effects varied with the bird species. Ten out of the thirty six species tested show a significant higher abundance value in organic agriculture. When the ratio was significantly different from zero, the abundance was 1.5–18 times higher in organic systems in comparison to conventional systems.

A methodological approach to identify cheap and accurate indicators for biodiversity assessment: application to grazing management and two grassland bird species

In response to environmental threats, numerous indicators have been developed to assess the impact of livestock farming systems on the environment. Some of them, notably those based on management practices have been reported to have low accuracy. This paper reports the results of a study aimed at assessing whether accuracy can be increased at a reasonable cost by mixing individual indicators into models. We focused on proxy indicators representing an alternative to the direct impact measurement on two grassland bird species, the lapwing Vanellus vanellus and the redshank Tringa totanus. Models were developed using stepwise selection procedures or Bayesian model averaging (BMA). Sensitivity, specificity, and probability of correctly ranking fields (area under the curve, AUC) were estimated for each individual indicator or model from observational data measured on 252 grazed plots during 2 years. The cost of implementation of each model was computed as a function of the number and types of input variables. Among all management indicators, 50% had an AUC lower than or equal to 0.50 and thus were not better than a random decision. Independently of the statistical procedure, models combining management indicators were always more accurate than individual indicators for lapwings only. In redshanks, models based either on BMA or some selection procedures were non-informative. Higher accuracy could be reached, for both species, with model mixing management and habitat indicators. However, this increase in accuracy was also associated with an increase in model cost. Models derived by BMA were more expensive and slightly less accurate than those derived with selection procedures. Analysing trade-offs between accuracy and cost of indicators opens promising application perspectives as time consuming and expensive indicators are likely to be of low practical utility.

Do the effects of crops on skylark (Alauda arvensis) differ between the field and landscape scales

The promotion of biodiversity in agricultural areas involves actions at the landscape scale, and the management of cropping patterns is considered an important means of achieving this goal. However, most of the available knowledge about the impact of crops on biodiversity has been obtained at the field scale, and is generally grouped together under the umbrella term “crop suitability.” Can field-scale knowledge be used to predict the impact on populations across landscapes? We studied the impact of maize and rapeseed on the abundance of skylark (Alauda arvensis). Field-scale studies in Western Europe have reported diverse impacts on habitat selection and demography. We assessed the consistency between field-scale knowledge and landscape-scale observations, using high-resolution databases describing crops and other habitats for the 4 km2 grid scales analyzed in the French Breeding Bird Survey. We used generalized linear models to estimate the impact of each studied crop at the landscape scale. We stratified the squares according to the local and geographical contexts, to ensure that the conclusions drawn were valid in a wide range of contexts. Our results were not consistent with field knowledge for rapeseed, and were consistent for maize only in grassland contexts. However, the effect sizes were much smaller than those of structural landscape features. These results suggest that upscaling from the field scale to the landscape scale leads to an integration of new agronomic and ecological processes, making the objects studied more complex than simple “crop ∗ species” pairs. We conclude that the carrying capacity of agricultural landscapes cannot be deduced from the suitability of their components.