Crop growth simulation models have become increasingly important as accepted tools with uses in education, research, crop management, industry, and government policy decisions related to improving and sustaining agricultural production of food, feed, fiber, and ecosystem services. This chapter begins with a brief review of history of crop modeling that has led to excellent collaborations among the world’s crop modeling community. It discusses present and future potential uses of crop models in education, academic research, plant breeding, strategic crop management, in-season advisory tools, famine risk tools, climate change impact assessment, ecosystem services, sustaining long-term soil fertility, and integration with livestock and economic models. It concludes with a look to the future with a review of what is new in crop models, the processes that need improvement, along with suggestions of new features needed.
Price: £25.00
Publisher: Burleigh Dodds Science Publishing
Imprint: Burleigh Dodds Science Publishing
Series: Burleigh Dodds Series in Agricultural Science
Publication Date:
02 December 2019
ISBN: 9781786765284
Format: eBook
BISACs:
TECHNOLOGY & ENGINEERING / Agriculture / Sustainable Agriculture, Agronomy and crop production, TECHNOLOGY & ENGINEERING / Agriculture / Agronomy / Crop Science, Sustainable agriculture
1 Introduction 2 The AgMIP decade of multiple crop model evaluations and improvements 3 Expanding the role of agroecosystems modeling 4 Crop models in the classroom 5 Crop modeling for plant breeding and genetics 6 The role of crop models in strategic crop planning and management 7 The role of crop models in real-time decision-making by agricultural enterprises 8 The role of crop models in government policy: predicting famine risk 9 The role of crop models in government policy: predicting and mitigating the impacts of climate change 10 The role of crop models in assessing ecosystem services and resource use 11 The role of crop models in assessing long-term soil fertility and sustainability 12 The impact of machine learning and big data on agroecosystem models 13 Connecting agroecosystem models to other components of farming: livestock and whole-farm models 14 What is new in modeling agroecosystems? 15 What improvements are needed in crop models? 16 Where to look for further information 17 References
