Modelling root distribution and nitrogen uptake

Plant soil and atmosphere models are commonly used to predict crop yield and environmental consequence. Such models often include complex modelling modules for water movement, soil organic matter turnover and, above ground plant growth. However, the root modelling in these models are often very simple, partly due to a limited access to experimental data. We present a two-dimensional model for root growth and proliferation. The model focuses on annual crops, and attempt to model root growth of the crops and its significance for N uptake from different parts of the soil volume

Simulating Root Density Dynamics and Nitrogen Uptake – Can a Simple Approach be Sufficient?

The modeling of root growth in many plant–soil models is simple and with few possibilities to adapt simulated root proliferation and depth distribution to that actually found with different crop species. Here we propose a root model, developed to describe root growth, root density and nitrogen uptake. The model focuses on annual crops, and attempts to model root growth of different crop species and row crops and its significance for nitrogen uptake from different parts of the soil volume

A model analysis on nitrate leaching under different soil and climate conditions and use of catch crops

The use of crops and catch crops with deep rooting can strongly improve the possibility of retaining nitrate-N that will otherwise be leached to the deeper soil layers and end up in the surrounding environment. But will it always be an advantage for the farmer to grow a catch crop? This will depend on factors such as soil mineral nitrogen level, soil water holding capacity, winter precipitation, rooting depth and N demand of the scceeding crop. These factors interact, and it can be very difficult for farmers or advisors to use this information to decide whether growing a catch crop will be beneficial. To analyse the effect of catch crops under different Danish soil and precipitation conditions, we used the soil, plant and atmosphere model Daisy

Unobserved Heterogeneity in the Binary Logit Model with Cross-Sectional Data and Short Panels: A Finite Mixture Approach

This paper proposes a new approach to dealing with unobserved heterogeneity in applied research using the binary logit model with cross-sectional data and short panels. Unobserved heterogeneity is particularly important in non-linear regression models such as the binary logit model because, unlike in linear regression models, estimates of the effects of observed independent variables are biased even when omitted independent variables are uncorrelated with the observed independent variables. We propose an extension of the binary logit model based on a finite mixture approach in which we conceptualize the unobserved heterogeneity via latent classes. Simulation results show that our approach leads to considerably less bias in the estimated effects of the independent variables than the standard logit model. Furthermore, because identification of the unobserved heterogeneity is weak when the researcher has cross-sectional rather than panel data, we propose a simple approach that fixes latent class weights and improves identification and estimation. Finally, we illustrate the applicability of our new approach using Canadian survey data on public support for redistribution.binary logit model; unobserved heterogeneity; latent classes; simulation