Skip to product information
1 of 1

Decision support systems (DSS) for better fertiliser management

Regular price £25.00
Sale price £25.00 Regular price £25.00
Sale Sold out
This chapter reviews some of the approaches used by DSS to determine fertiliser application decisions. The chapter highlights direct methods and indirect techniques: simulation models, yield foreca...
Read More
  • Format:
  • 27 April 2020
View Product Details
This chapter reviews some of the approaches used by DSS to determine fertiliser application decisions. The chapter highlights direct methods and indirect techniques: simulation models, yield forecasts using data-driven approaches and yield forecasts based on water supply. The chapter includes two case studies to estimate season-specific nitrogen requirements of wheat crops at a within-field scale in Australia. These models forecast yield in two key periods of the season in which farmers make decisions for fertiliser applications – pre-sowing, and mid-season.
files/i.png Icon
Price: £25.00
Publisher: Burleigh Dodds Science Publishing
Imprint: Burleigh Dodds Science Publishing
Series: Burleigh Dodds Series in Agricultural Science
Publication Date: 27 April 2020
ISBN: 9781786767301
Format: eBook
BISACs:

TECHNOLOGY & ENGINEERING / Agriculture / Agronomy / Crop Science, Agronomy and crop production, TECHNOLOGY & ENGINEERING / Agriculture / Sustainable Agriculture, Sustainable agriculture

REVIEWS Icon

1 Introduction 2 Direct methods for determining crop nitrogen requirements for decision support 3 Indirect methods for determining crop nitrogen requirements for decision support: simulation models 4 Indirect methods for determining crop nitrogen requirements for decision support: yield forecasts using data-driven approaches 5 Indirect methods for determining crop nitrogen requirements for decision support: yield forecasts based on water supply 6 Decision support in action: case studies 7 Case study 1: nitrogen fertiliser applications using a data-driven approach 8 Case study 2: nitrogen fertiliser decision-making based on soil moisture predictions 9 Comparing the two approaches 10 Conclusions and future trends 11 References