Models of crop growth and development were conceived originally for scientific purposes. Typically, they describe the mechanisms of crop production, development from emergence through tuber initiation to senescence determined by temperature and day-length. Growth is driven by solar radiation intercepted by the foliage. Yields are enhanced by the availability of water and nutrients and may be reduced by pests, diseases and weeds. The scientific models describing the processes involved are leaving the research institutes and increasingly are becoming a means of knowledge transfer for students, and most importantly, to growers and their intermediaries such as extensionists and consultants. Many decision support systems (DSS) have a mechanistic model core that assures their robustness and reliability. This book gives an overview of model-based DSS in potato production. Decision support systems are used by the processing industry to guide them to promising production areas and by breeders to identify the ideal genotype for such environments. Consultants and soil laboratories use them as well as farmers to optimize the use of nitrogen, water and chemicals to control insects, nematodes, late blight and weeds. The systems, making use of models and sensing techniques, improve yield and quality while allowing their users to improve the efficiency of use of resources, thus generating positive effects for profits and for the environment. The book also gives examples of new introductions of DSS and farmers responses. The book is intended for researchers wanting to bring their models to practice, students to learn bout DSS, intermediaries and growers to improve the performance of the potato industry or of other commodities for which potato serves as an example.

Table of Contents:
Preface 13; 1. Role of decision support systems in potato production 17; A.J. Haverkort and D.K.L. MacKerron; Types of decisions 17; Use of DSS to increase sustainability in potato chains 19; DSS in supply chain management 22; Future developments in decision support 23; References 26; 2. Potato-zoning: a decision support system on expanding the potato industry through agro-ecological zoning using the LINTUL simulation approach 28; A.J. Haverkort, A. Verhagen, C. Grashoff and P.W.J. Uithol; Introduction 29; Approach 33; Zoning explorations 36; Conclusions and perspectives for future development 41; References 43; 3. Crop-reflection-based DSS for supplemental nitrogen dressings in potato production 46; R. Booij and D. Uenk; Introduction 47; The system 48; Conclusion 52; References 53; 4. Development of a decision support system for nitrogen management on potatoes 58; C. Chambenoit, F. Laurent, J.M. Machet and H. Boizard; Introduction 55; Evaluation of a potato crop's nitrogen requirement 56; Evaluation of soil nitrogen supply for potato crops 60; Validation of the results 63; Decision support systems developed in France for potato growers 64; Conclusions 66; Acknowledgements 67; References 67. 5. Management of N-fertilization of the potato crop using total N-advice software and in-season chlorophyll-meter measurements 68; J.P. Goffart and M. Olivier; Introduction 69; Quantitative basis of the balance-sheet method as developed in the Azobil software 71; Detailed descriptions 73; Description of the decision support system in practice 75; Experiences with the strategy developed 80; Prospects 81; References 82; 6. Implementation and testing of the Potato Calculator, a decision support system for nitrogen and irrigation management 88; P.D. Jamieson, P.J. Stone, R.F. Zyskowski, S. Sinton and R.J. Martin; Introduction 85; The model 86; Experimental validation 89; Results and discussion 91; Afterword 97; Acknowledgements 97; References 98; 7. Automated on-farm assessment of tuber size distribution 100; Bruce Marshall and Mark W. Young; Background 101; System development 102; Evaluation 106; Summary and future development 115; Acknowledgements 116; References 116; 8. MAPP, the Management Advisory Package for Potatoes: decision support means giving informed options not making decisions 118; D.K.L. MacKerron, B. Marshall and J.W. McNicol; Introduction and background 119; Description 120; Design phase 123; Primary inputs 124; Possible motives for using MAPP 126; Interaction with users 138; Conclusions 140; References 140. 9. Projecting PCN population changes and potato yields in infested soils 142; Martin J. Elliott, David L. Trudgill, James W. McNicol and Mark S. Phillips; Introduction 143; Development of the models 145; The Decision Support System (DSS) 146; Example 148; Future developments 150; User base 150; Benefits of this program 151; References 151; 10. A geo-referenced Decision Support System for nematodes in potatoes 154; T.H. Been and C.H. Schomaker; Introduction 155; Outline 157; Focus model 160; Geo information and monitoring 164; Experience 165; Prospects 167; References 167; 11. PLANT-Plus: Turn-key solution for disease forecasting and irrigation management 168; P. Raatjes, J. Hadders, D. Martin and H. Hinds; Introduction 169; Disease forecasting models & principles 170; Principles for irrigation scheduling 176; Quality of weather forecasts 176; Experiences and results 177; Commercial evaluation of P. infestans model 179; Field trials and commercial evaluations of the A. solani model 181; Field trials and commercial evaluations of the irrigation management model 182; Future developments and constraints 183; References 183; 12. MLHD, a decision support system for rational use of herbicides: developments in potatoes 186; C. Kempenaar and R. van den Boogaard; Introduction 187; The MLHD concept 188; The MLHD model 189; The PS1 sensing technique 190; On farm research 191. Potato haulm killing and MLHD 192; MLHD online 193; Prospects 195; References 196; 13. IDEOTYPING-POTATO a modelling approach to genotype performance 198; A.J. Haverkort and C. Grashoff; Introduction 199; The science behind the DSS 201; Description of the target area 202; Ideotyping runs 204; Results of model runs 206; Future developments 210; Acknowledgements 211; References 211; 14. Necessity and sufficiency or the balance between accuracy and practicality 213; D.K.L. MacKerron; Introduction 213; The problem of accuracy, and uncertainty. (Modellers aim for precision and forget errors) 214; What is necessary and what is sufficient? Cases drawn from some of the DSS described in this book 217; Other particular examples of features of DSS 221; Conclusions 222; References 223; Authors 225; Index 229.