Landscape classification tools


There are three parts to this research strand. First, a robust model for estimating legume yield and suitability across various New Zealand landscapes is being developed. Another team is quantifying and mapping landscape micro-scale indicators (soil temperature and moisture) that guide which legume forage mixes are likely to do best in different parts of the landscape. The final element is a landscape classification tool that includes measures of natural capital, such as biodiversity.

Modelling legume yield

This real-time model draws on 20 years of lucerne, soil and water data from Lincoln University and on-farm experiments.

It aims to answer questions around legume forages’ impact on production, environment, climate change, nutrient leaching and carbon sequestration.

The model will be publicly available and is expected to be used widely by agribusiness professionals, policy makers and researchers, rather than directly by farmers.

Lead scientist: Professor Derrick Moot

Team members: Jian Liu (Frank) , Dr Edmar Teixeira

Micro-scale indicators

The project recognises that hill country farms are diverse landscapes. It is designed to give farmers the tools to make more robust decisions around suitable locations for various forage legumes.

Micro-scale indicators – soil temperature and moisture – are used to help guide farmers around which forage mixes are likely to do well in specific areas of their farm.

Ideally, farmers will monitor soil temperature and moisture ongoing, to make more effective decisions that lead to improved economic, environmental and social outcomes.

Team members: James Barringer, Dr Jagath C Ekanayake, Dr Nathan Odgers

Measures of natural capital

This landscape classification tool includes three critical measures: soil health, organic matter, and terrestrial and aquatic biodiversity. In this way, farmers have environmental insight around future challenges to farming, as well as the knowledge and tools required to monitor and report on their businesses.

Lead scientist: Dr Alec Mackay