Research undertaken through the Hill Country Futures Partnership programme has led to the development of national suitability maps showing where lucerne crops can be grown.
The research involved a collaboration between scientists from Plant & Food Research (PFR), Lincoln University, Manaaki Whenua-Landcare (MW-LC) and NIWA.
The maps, developed by a team led by senior scientist Dr Edmar Teixeira of Plant & Food Research, also provide an estimate of potential yields for lucerne crops across different regions.
Lucerne was used in the study with the aim of developing a method to identify other suitable legumes and growing areas in New Zealand, and factors that could affect yield. The goal now is to extend the programme to other legumes.
The $8.1m Hill Country Futures Partnership programme is co-funded by Beef + Lamb New Zealand, the Ministry of Business, Innovation and Employment, PGG Wrightson Seeds and Seed Force New Zealand.
The programme is focused on future proofing the profitability, sustainability and wellbeing of New Zealand’s hill country farmers, their farm systems, the environment and rural communities.
Dr Teixeira’s main research focus is biophysical processes in cropping systems. He uses biophysical modelling to simulate crop performance across different locations and time periods. The goal is to provide new insights to help manage cropping systems.
For this project, the team has been spatially simulating lucerne growth across climates and soils in New Zealand to map its suitability as a forage crop.
The research used historical daily weather data from NIWA and soil types from Manaaki Whenua-Landcare (MW-LC) using the Agricultural Production Systems sIMulator (APSIM), co-developed by Plant & Food Research (PFR), to simulate crop growth.
“Forage legumes are important resources in New Zealand hill country and agricultural areas in general,” says Dr Teixeira. “They fix nitrogen from the atmosphere and provide a high quality feed for livestock – a combination of positive environmental and economic outcomes.”
“For farmers, it is important to know the potential of production of different legume crop options within a given region and understand how yields change under different climate, soil and management combinations.
“Our programme aimed to develop new methods to simulate yield potentials of legumes across New Zealand at large – landscape – scale. This knowledge creates benchmarks and helps identify main limiting factors and causes of yield gaps. Also, yield variability across many years can be assessed across environments with this method, which is a measure of risk of production.”
Lucerne crops were used as the team’s proof of concept because of the more abundant data for the crop available in New Zealand. This included developing the models and also running APSIM-lucerne within a spatial framework called ATLAS, developed at Plant & Food Research in a high performance computing environment.
“The method enables the team to link the agricultural models with long-term NIWA climate data at five kilometre resolution across NZ and also represent different soils, such as from the S-map digital soil database developed at MW-LC.
“In this specific application, we created a first set of national suitability and yield maps for lucerne, which show where lucerne crops can grow and provide an estimate of their potential productivity across different regions.”
The prototype created in the programme can now be used to ask specific questions through virtual experiments, such as the effects of changing defoliation management in different regions.
It also enables the team to explore crop responses to new climates – for instance, using NIWA climate change projections instead of historical climate – and it can also be expanded to represent other legume species.
Work is ongoing to test and improve the models.
“It is important to note that such models are simplified representations of reality and results must be interpreted accordingly, within the scope where they were developed,” said Dr Teixeira.
“Fit-for-purpose models can be seen as useful resources to extend our understanding beyond what empirical data provides. They are built upon field datasets which have to continue to be collected with high quality to help testing and improving models in the future.”