A sulphur amino acid deficiency changes the amino acid composition of tissue protein in growing pigs


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Modeling the spatial distribution of cropping systems at large regional scale: a case of crop sequence patterns

Introduction

Over the past decade, the scientific interest of the location of cropping systems, including both the crop sequences and the crop management systems, has been increasingly noticed by researchers who associated crop rotations instead of single crop for representing cropping patterns in their models (nitrogen fluxes, animal habitat preference, etc.) and further analyzing the environmental impacts of agricultural production systems. However, in existing modeling frameworks of land-use/cover change, especially agricultural land-use change, few models integrate agricultural land management practices such as crop rotations [START_REF] Schonhart | Crop Rota -A crop rotation model to support integrated land use assessments[END_REF]. In this paper, we aim at investigating crop sequence patterns at large regional scale by performing statistic technique on surveyed land-cover data.

Materials and Methods

The land-cover data was derived from an annual French national land-cover census data, Teruti, from 1992 to 2003. The land unit studied was the French agricultural district and all of the 430 agricultural districts were incorporated in this research. By combining the application of CARROTAGE (Le [START_REF] Ber | Studying crop sequences with CARROTAGE, a HMM-based data mining software[END_REF], a second-order Hidden Markov Model based on stochastic theory and a classic statistic technique, Principal Component methods prior to Agglomerative Hierarchical Methods (AHC) using software R package FactoMineR [START_REF] Husson | Exploratory Multivariate Analysis by Example Using R, Computer Science and Data analysis Series[END_REF], we developed a modeling approach to identify the spatial distribution of crop sequences at the French metropolitan scale. We considered 3-year crop sequences as distinct land-use types. Firstly, we applied CARROTAGE for discovering the temporal regularities of land-use within each district. The probabilities of occurrences of 3-years land-use successions previously calculated were used as independent variables to build a table for classifying districts. Secondly, we performed the Principal Component Analysis (PCA) on this table in which 510 principal crop sequences were used as active variables. In order to facilitate the interpretation of clusters, we introduced 6 permanent non-agricultural land-use successions which contained forest, water, artificial area and stone area as supplementary variables. Finally, we performed a hierarchical clustering on the principal components using the function 'HCPC' integrated in FactoMineR with predefining the number of clusters between 20 and 30. We then mapped this classification with ArcGIS.

Results

The first 119 components of the PCA explained 80.2% of the total inertia and thus were used for clustering. With the optimal level of division suggested by the HCPC function, a classification of districts into 20 clusters was produced. We observe the differentiation of crop sequences among regions (Fig 1). We also detect the livestock farming systems zone with crop sequences predominantly involving maize and temporary pasture, the cereal and vegetable farming systems zone including wheat, barley, sugar beet and pea based crop sequences, etc. Details of the clusters' description are shown in Table 1.

Conclusion

We emphasize that the crop sequence patterns identified in this study seem plausible in comparison to the result of the farming Systems survey OTEX which was published by the French Ministry of Agriculture in 2000. Our modeling approach can be considered as a generic approach tested at French metropolitan scale, and it provides a useful tool for agronomists to represent past agricultural land-use patterns at the global scale.


Simulating the dispersal of Monochamus galloprovinciallis based on its flight mill performance and testing several management scenarios

The potential spread of the pine wood nematode, Bursaphelenchus xylophilus, and its associated pine wilt disease are strongly associated with the flight capacity of the insect vector. Although some data were available from congeneric species in North America and Asia, the flight performances of the European vector, Monochamus galloprovinciallis, were largely unknown. They were assessed with flight mill experiments and used to fit a dispersal kernel. A stochastic individual-based model was then developed to simulate the trajectory of adults over one season. With this dispersal model and a transmission function of the nematode, the area where the vector can transmit the pine wood nematode can be determined and several forest management scenarios to contain its spread can be tested. Although this dispersal model is still at an exploratory stage, and beetle's dispersal capacity might be overestimated with flight mill, this approach contributes to a better understanding of vector's dispersal pattern and can be used to test the effects of several management scenarios. This dispersal model is a first step towards the development of a refined spread model of the nematode and the disease at larger spatial and temporal scales in Europe.