Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

http://abe.ufl.edu/Faculty/jjones/ABE_5646/Xtra%20files/The%20DSSAT%20Cropping%20System%20Model.pdf

The DSSAT cropping system model

Summary
The soil is important in sequestering atmospheric CO2 and in emitting trace gases (e.g. CO2, CH4 and N2O) that are radiatively active and enhance the ‘greenhouse’ effect. Land use changes and predicted global warming, through their effects on net primary productivity, the plant community and soil conditions, may have important effects on the size of the organic matter pool in the soil and directly affect the atmospheric concentration of these trace gases.

A discrepancy of approximately 350 × 1015 g (or Pg) of C in two recent estimates of soil carbon reserves worldwide is evaluated using the geo-referenced database developed for the World Inventory of Soil Emission Potentials (WISE) project. This database holds 4353 soil profiles distributed globally which are considered to represent the soil units shown on a 1/2° latitude by 1/2° longitude version of the corrected and digitized 1:5 M FAO–UNESCO Soil Map of the World.

Total soil carbon pools for the entire land area of the world, excluding carbon held in the litter layer and charcoal, amounts to 2157–2293 Pg of C in the upper 100 cm. Soil organic carbon is estimated to be 684–724 Pg of C in the upper 30 cm, 1462–1548 Pg of C in the upper 100 cm, and 2376–2456 Pg of C in the upper 200 cm. Although deforestation, changes in land use and predicted climate change can alter the amount of organic carbon held in the superficial soil layers rapidly, this is less so for the soil carbonate carbon. An estimated 695–748 Pg of carbonate-C is held in the upper 100 cm of the world's soils. Mean C: N ratios of soil organic matter range from 9.9 for arid Yermosols to 25.8 for Histosols. Global amounts of soil nitrogen are estimated to be 133–140 Pg of N for the upper 100 cm. Possible changes in soil organic carbon and nitrogen dynamics caused by increased concentrations of atmospheric CO2 and the predicted associated rise in temperature are discussed.

https://library.wur.nl/isric/fulltext/isricu_t47d6414d_001.pdf

Total carbon and nitrogen in the soils of the world

A remote sensing surface energy balance algorithm for land (SEBAL)-1. Formulation

An overview of APSIM, a model designed for farming systems simulation

Policy makers and farmers have an interest in making ex-ante assessments of the outcomes of their choices in terms of policy and farm plan. This interest mainly concerns the assessment of socio-economic and environmental performance of farms as a result of innovations and policies. Mathematical models based on systems analysis are suited to explore and assess uncertain future states of systems. A Bio-Economic Farm Model (BEFM) is defined as a model that links formulations describing farmers’ resource management decisions to formulations that describe current and alternative production possibilities in terms of required inputs to achieve certain outputs and associated externalities. Currently many descriptions and applications of BEFMs are being published. A BEFM that is easy to transfer between locations or farm types is called generic, which we define as ‘being able to deal with different scales, locations, functionalities, and levels of detail. Although some model studies claim that their model is easily transferable to other regions and farm types, there is little evidence from literature supporting these claims. A generic bio-economic farm model should be able to adequately represent arable, livestock and perennial activities, current agricultural activities and future alternative activities, different objective functions, different resource and policy constraints, future policies and technological innovations as scenarios and should include good calibration procedures. In SEAMLESS the Farming Systems SIMulator (FSSIM) has been developed as a generic and transferable model that can easily be extended with new features and re-used across data-sets, farm types and locations. In this deliverable we (i) describe FSSIM and its design as an integrated generic bio-economic farm model, (ii) briefly describe each of the components of FSSIM and provide references to relevant deliverables and publications for more detail and (iii) evaluating FSSIM on criteria for generic models by describing applications of FSSIM.

/pdf/integration-of-all-fssim-components-within-seamless-if-and-a-4dtvyx14ej.pdf

Integration of all FSSIM components within SEAMLESS-IF and a stand alone Graphical User Interface for FSSIM

In this paper, the land use planning cycle is introduced and some recent efforts at developing appropriate methodologies for supporting some of its distinct phases are illustrated. The examples still largely bear an academic character, but since there is increasing demand by policy makers for integrated land use analysis studies, they may serve as building blocks for development of operational methodologies for land use policy ormulation and analysis. Their potential impacts on planning procedures and chievement of land use objectives are high, particularly when they are further developed and operationalized in settings that allow participation and involvement of the various user groups

/pdf/new-approaches-to-support-development-of-sustainable-land-5cjwmhja89.pdf

New Approaches to Support Development of Sustainable Land Use Systems

Crop growth models help to formalize expert knowledge to assess and design cropping systems with multiple criteria (i.e. production and externalities). The process of modelling to develop an operational tool requires different skills from conceptualization of the biophysical system to the coding of the equations. The focus of this paper is on the procedure to develop a new crop model from an existing one, using a crop modelling framework. First, the conceptual model is co-developed by scientists of different backgrounds, having theoretical and/or practical knowledge of the crop. Then, to translate the conceptual model into an operational tool, there is a need for communication between these experts and software engineers coding the model. We illustrate the approach with a case study of changing a wheat crop model into a pea model. (Resume d'auteur)

Developing new crop models within a flexible crop modelling framework: use of crop physiological knowledge to change a wheat model into a pea model

Regional crop modelling.

Productivity of Sahelian rangelands. A study of the soils, the vegetations and the exploitation of that natural resource. PPS course book. Volume I. Theory. Volume II. Exercises.

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