Integration of a Cropping Systems Simulation Model and a Relational Database for Simple Farm‐Scale Analyses

Abstract

Agricultural simulation models are important tools in research, extension, policy formulation, and teaching. Their integration with relational databases allows carrying out complex and comprehensive data analyses of model inputs and outputs. In this article, we describe the integration of a dynamic cropping systems simulation model (CropSystVB) and a relational database (Microsoft Access), with the aim of developing a tool for simple applications at the farm scale. The data model, designed with the entity-relationship framework, represents the structure of the data and allows for organized and retrievable storage of inputs and outputs of the simulation model. In addition to standard CropSystVB's inputs (soil, crop, weather, management events), the integrated tool also requires input data to describe the nutrients produced by livestock and allows for spatially variable fields, soils, and management options. Farm simulation scenarios can be defined, including the actual and alternative configurations of animal breeds, rotations, and crop management. One or more rotations can be simulated for each scenario; each rotation is located on a homogeneous area (a field, or part of it, or a group of fields); each area is described with relevant soil and weather inputs. The Microsoft Access implementation of the integrated tool (www.bsyse. wsu.edu/cropsyst; verified 15 June 2007) includes a Visual Basic for Applications version of the model and tables and forms to store and manage data. An example simulation in an animal farm with six scenarios, obtained by the combination of three animal loads and two irrigation systems (surface and sprinkler), is presented to illustrate its use.

Diachronic Analysis of Farmers’ Strategies within a Protected Area of Central Italy

“…Consequently, the decision of choosing the farm as a subject of investigation in the study of production systems at territorial scale is related to the purpose of bringing out and analysing the farmer's strategies (Bockstaller et al, 1997;Bechini and Stöckle, 2007;Kempen et al, 2011;Righi et al, 2011). At smaller scales those characteristics could be biased by data aggregation thus reducing their variability, while at grater scale they could be influenced by specific cultural conditions which make the observed behaviours more difficult to be interpreted (Volk and Ewert, 2011).…”

Ad Hoc Modeling in Agronomy: What Have We Learned in the Last 15 Years?

“…Bechini and Stockle (2007) proposed a technique to integrate a crop model and a relational database. Their approach makes it possible to automate the construction of the model input files, and thus the simulation of, for example, crop rotations or spatially specific simulations (fields with different soil types), using queries.…”

“…Group IV, “Methodological elements for ad hoc modeling” contains only three articles that are relatively recent and are directly focused on approaches likely to facilitate ad hoc modeling (Park et al, 2005; Bechini and Stockle, 2007; Adam et al, 2012). They will be discussed in the next section, together with articles not explicitly referring to but applying and/or discussing the principles expressed by Sinclair and Seligman.…”

Impact of Agromanagement Practices on Rice Elongation: Analysis and Modelling

“…Indeed, even without considering the management effect on PH, few approaches are available for the simulation of this variable, and in most cases they are markedly empirical, providing S-shaped curves driven by air temperature (Kotera and Nawata, 2007), phenology (Lizaso et al, 2005), or leaf area index (e.g., Bechini and Stöckle, 2007;Confalonieri et al, 2005). Starting from the early 1980s, they have been increasingly used to support crop management at field level and to evaluate and compare alternative management scenarios.…”

“…Despite the effect of management practices on rice PH (e.g., floodwater and N management, sowing density) and of their implications on yield potential, no simulation models are available to reproduce the biophysical processes involved. Indeed, even without considering the management effect on PH, few approaches are available for the simulation of this variable, and in most cases they are markedly empirical, providing S-shaped curves driven by air temperature (Kotera and Nawata, 2007), phenology (Lizaso et al, 2005), or leaf area index (e.g., Bechini and Stöckle, 2007;Confalonieri et al, 2005).…”