Trade-off analysis in the Northern Andes to study the dynamics in agricultural land use

In this paper we hypothesize that land use change can be induced by non-linearities and thresholds in production systems that impact farmers' decision making. Tradeoffs between environmental and economic indicators is a useful way to represent dynamic properties of agricultural systems. The Tradeoff Analysis (TOA) System is software designed to implement the integrated analysis of tradeoffs in agricultural systems. The TOA methodology is based on spatially explicit econometric simulation models linked to spatially referenced bio-physical simulation models to simulate land use and input decisions. The methodology has been applied for the potato-pasture production system in the Ecuadorian Andes. The land use change literature often describes non-linearity in land use change as a result of sudden changes in the political (e.g. new agricultural policies) or environmental setting (e.g. earthquakes). However, less attention has been paid to the non-linearities in production systems and their consequences for land use change. In this paper, we use the TOA system to study agricultural land use dynamics and to find the underlying processes for non-linearities. Results show that the sources of non-linearities are in the properties of bio-physical processes and in the decision making-process of farmers.     

Effects of soil variability and weather conditions on pesticide leaching--a farm-level evaluation

In line with European regulations, Dutch law imposes an environmental threshold of 0.1 microg L(-1) on pesticide concentrations in ground water. During registration, the risk of exceeding this threshold is assessed through simulations for one or a few standard scenarios that do not reflect spatial variability under field conditions. The introduction of precision agriculture, where soil variability is actively managed, can increase control over pesticide leaching. This study presents a step-wise evaluation of the effects of soil variability and weather conditions on pesticide leaching. The evaluation was conducted on a 100-ha arable farm and aimed at identifying opportunities for precision management. As a first step, a relative risk assessment identified pesticides presenting a relatively high risk to the environment. Second, the effect of weather conditions was analyzed through 20 years of simulations for three distinct soil profiles. Results were summarized in cumulative probability plots to provide a probabilistic characterization of historical weather data. The year matching 90% probability (1981) served as a reference to simulate pesticide leaching from 612 soil profiles. After interpolation, areas where concentrations exceeded the environmental threshold were identified. Out of a total of 19 pesticides, isoproturon [N-dimethyl-N'-(4-(1-methylethyl)phenyl)urea], metribuzin [4-amino-6-tert-butyl-3-(methylthio)-as-triazin-5(4H)-one], and bentazon [2,1,3-benzothiadiazin-4(3H)-one, 3-isopropyl-, 2,2-dioxide] showed the highest risk for leaching. Leaching was strongly affected by soil variability at the within-field, field, and farm levels. Opportunities for precision management were apparent, but depended on the scale level at which environmental thresholds were implemented. When legislation is formulated in this issue, the presented step-wise evaluation can serve as a basis for identification and precision management of high-risk pesticides.