e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.

This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.

You can access and play with the graphs:

Discover all records
Home page

Title

Using computer simulation models to aid replant planning and harvest decisions in irrigated sugarcane

en
Abstract

Large commercial sugarcane operations face complex replant planning decisions. The replant operation is costly and limited resources must be employed where they are likely to produce the largest yield improvement. These decisions are complicated further by the need to evaluate the benefit across multiple cutting seasons. Typically, replant field selection is based on historical performance, and poorest yielding fields on poorer soils tend to be prioritised for replant. However, this approach might not maximise estate-wide productivity. The decision making process needs to consider: which fields should be selected for replant in which season; what varieties should they be replanted back to; and what long-term harvest sequence should be followed to minimise harvest age effects; to maximise sucrose production in current and future seasons. A replant planning decision_support framework was developed in CanePro, a commercially available Agricultural Management System, to assist with this complex task. Field selection was made by benchmarking actual field cane yields against potential yields discounted for soil type and ratoon using a soil type/ratoon matrix. Climatic potential yields were estimated using a simplified version of the CANEGRO crop simulation model. Each field's ideal replant ratoon age was estimated by maximising the total (across all fields) of the average (across all ratoons) expected yield of each field. Fields were assigned a replant date based on their ratio of current to ideal replant ratoon age within the estate's replant capacity constraints. Replant variety selection was made by optimising overall sucrose performance in a season using variety-specific sucrose curves. The harvest sequence was adapted to maximise overall sucrose production. To evaluate the methodology, four seasons of historical field data were obtained from a commercial operation in Swaziland. Actual estate practice was compared with the replant recommendations made using the CanePro framework. The relative performance of the scenarios was evaluated by comparing the overall sucrose yield simulated for each scenario. A 0.6% improvement was attributed to the CanePro field selection algorithm and a further 0.6% to the harvest sequencing algorithm.

en
Year
2011
en
Country
  • ZA
Organization
    Data keywords
    • information technology
    en
    Agriculture keywords
    • agriculture
    en
    Data topic
    • information systems
    • modeling
    en
    SO
    INTERNATIONAL SUGAR JOURNAL
    Document type

    Inappropriate format for Document type, expected simple value but got array, please use list format

    Institutions 10 co-publis
      uid:/0DJTXPGV
      Powered by Lodex 8.20.3
      logo commission europeenne
      e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
      Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.