Agriculture, Ecosystems and Environment 81 (2000) 1–3
Editorial
Spatial statistics for production ecology
La nature s’imite: une graine, jet´ee en bonne terre, produit; un principe, jet´e dans un bon esprit, produit; les nombres imitent l’espace, qui sont de nature si diff´erente.1
Blaise Pascal
At the moment we realize that the world will face major changes in land use in the coming decades. Agricultural production will need to meet the needs of the world population which is expected to double to 12 billion, and to meet the increasing demand as a result of continued economic growth and consumer demands. Agriculture needs to meet this rising de-mand on less land, with less resources such as water, while satisfying ever-tighter constraints with respect towards the quality of its products and the impact of production techniques on humans and environment. This will increase demands on the natural resources of soil, water and air which are being depleted and degraded in many parts of the world, threatening the continuity of agro-ecosystems. These issues require a thorough scientific analysis of ecosystems and the de-velopment of relevant options for agricultural produc-tion and conservaproduc-tion activities. Producproduc-tion ecology addresses sustainable agricultural production systems that are in harmony with the natural environment at different scales.
Production ecology aims at understanding agro- and natural ecosystems, while addressing socio-economic and ecological objectives in the society at various
1“Nature imitates itself: a grain sown in good earth multiplies
itself; a concept sown into a good mind likewise; numbers follow up space how diverse of origin they may be.”
levels of scale. It therefore focuses on sustainable development of the rural environment.
Production ecology with a focus on understanding agro- and (semi-)natural ecosystems at different scales provides new opportunities to enhance sustainabil-ity of agricultural and natural ecosystems. The term sustainability has got an operational meaning by iden-tifying clear goals for systems, and by defining quan-tifiable indicators for sustainability of agro-ecosystems and natural systems such as soil quality parameters, or environmental indicators that can be measured (e.g. nitrate in groundwater, biodiversity indicators). The basis of this approach is the strengthening of the eco-logical base of agricultural systems such as by using natural enemies for pest control, leguminous crops for N fixation etc. Production ecology has become a set of concepts and approaches from a combination of earth, climate and biological sciences.
Never any knowledge was delivered in the same order it was invented.
Francis Bacon
In a more practical way, modern farmers are in-creasingly aware of the consequences of their work to environment and ecology. This has led to modern ap-proaches towards farming, like precision agriculture and integrated pest and disease management. This new way of farming aims at competitive production in an environmentally friendly way and leads to decisions that are better supported by the most recent agricul-tural knowledge, and hence to more sustainable use of land.
Production ecology has led to new methodologies in scaling up and down processes, the use of spatial
2 A. Stein, J. Goudriaan / Agriculture, Ecosystems and Environment 81 (2000) 1–3
statistics for model calculations and interpolation, and for optimal sampling procedures. Production ecology has largely benefited from spatial statistics in various ways. First, it relies on the use of agricultural, crop and soil models. These models must be reliable, i.e. applicable at the proper scale, properly calibrated and validated and reliable data must be available. Second, basic input for these models is increasingly collected by remote sensing. Remote sensing data are of an in-creasing resolution (resolutions up to 0.75 m are now currently in use), and the numbers of bands is growing rapidly. Proper classification and segmentation proce-dures are still being developed. Finally, data stored in geographical information systems are used. Geograph-ical information systems are indispensable to combine sources of data and to visualize important spatial in-formation, sometimes even at agricultural equipment. Questions on reliability of many of these data need a statistical analysis to help find the solution. In particu-lar, spatial statistics is important in all these aspects of production ecology. Spatial statistics quantifies spatial uncertainty and supports problem solving in issues of scale and spatial modeling. Examples include
interpo-Fig. 1. Oakleaf fallen on fertile soil, by Trijnie Goudriaan.
lation of agricultural models to field or regional scales, quantification of uncertainty in GIS, upscaling, and use of remote sensing images for decision support. The idea of using statistics in an agricultural context from the seed to production is of course not new, as is illustrated by thought 119 of Pascal, from 1670.
In association with the International Statistical Insti-tute and sponsored by the Royal Netherlands Academy of Sciences and by the CT de Wit Research School for Production Ecology the international conference of Spatial Statistics for production ecology was orga-nized. It took place in Wageningen, the Netherlands, from 19 to 21 April 1999. Co-organizers of the meet-ing were Elisabeth Addink, Arnold Bregt and Steven de Jong. The aim was to present up-to-date develop-ments in spatial statistics for production ecology, to present on-going research and to discuss important problems to be addressed in the near future.
A. Stein, J. Goudriaan / Agriculture, Ecosystems and Environment 81 (2000) 1–3 3
describes a spatialisation approach using imprecise soil data for modelling crop yields over vast areas. At-tention focuses on a wine growing region in southern France. A soil map is used as prior information. The second paper assesses spatial variability of a Mediter-ranean forest at three different levels, and addresses the important issue of scale in production ecology. The third paper considers modeling, interpolation and stochastic simulation in space and time of global solar radiation. Geostatistical modelling in space and time is an essential element for factors that influence crop specific properties. This paper covers a relatively large area of land, i.e. the Po valley, in northern Italy. The fourth paper describes uncertainties in the appraisal of water availability and consequences for simulated sug-arcane potential in Sao Paulo State, Brazil. We note that spatial variability is the issue that connects these papers, at several different scales, and with an im-portant extension into the space-time direction. Crops include forests, grapes and sugarcane, but input vari-ables for crop modelling are addressed as well. Fig. 1. The second group highlights uses of new tech-niques in crop modelling, like remote sensing and geographical information systems. The fifth paper studies effects of variability of soils and crops on a procedure of site specific adjustment of a crop model using remote sensing. A general crop model is used, to which remote sensing data are added as explanatory variables. The final paper addresses the final stage in
the modelling process, by making a sensitivity and uncertainty analysis in spatial modelling based on GIS. The GIS turns out to be useful to supply the necessary data and easy facilities for handling.
In this special issue, papers are collected that were presented at this conference with a clear agronomical signature. Papers of a more statistical character will be published as a special issue of Environmental and Ecological Statistics, in the near future.
The special issue is illustrated with a work of art, a charcoal drawing by Trijnie Goudriaan, who created it especially for this issue. It shows an oakleaf fallen onto a fertile soil, and symbolizes the processes of life and decomposition, that at the same time thrive on heterogeneity and give rise to it. It can also be viewed as a picture of heterogeneous fractal dimension, apart from any consideration of scale.
Alfred Stein∗, Jan Goudriaan
Wageningen University Laboratory of Soil Science and Geology PO Box 37 6700 Wageningen The Netherlands
∗Corresponding author. Tel.:+31-317-484410;
fax:+31-317-482419.
E-mail address: alfred.stein@bodlan.beng.wau.nl
