Chestnut - crop suitability maps, rules and yield information

Description

Crop (Chestnut) suitability information includes

1) map of suitability categories (1 - Well suited, 2 - Suitable, 3 - Marginally Suited, 4 - Unsuitable)

2) rules used to generate the suitability maps

3) map of suitability categories where biophyical constraints are managed through irrigation, artificial drainage, or liming

4) map of the potential yield (provided as a lower/upper range)

5) map of the potential yield where biophyical constraints are managed through irrigation, artificial drainage, or liming (provided as a lower/upper range)

 

Date: Sept 2021 Version: v1

Owner: MPI

Contact: Dr Steve Thomas, Plant and Food Research

Preview Image

 

Dataset attributes

Spatial extent New Zealand
Spatial resolution 500 m pixel size, 5 km climate grid and FSL polygons
Temporal extent 30 year average, climate data 1971-2000
Temporal resolution Yield values are mean annual estimates
Evaluation method (Validation) Expert assessed
Evaluation result (Numeric) N/A
Evaluation result (Categorical) N/A
Uncertainty method None
Uncertainty data format (Numeric) Yield information: provided as a range (low - upper each provided as a raster layer)
Uncertainty data format (Categorical) Suitability: Moderate reliability at national scale (there is a reasonable understanding of crop requirements but key inputs have limited accuracy)

 

Methodology

The land suitability concept is used to characterise the degree of fitness of a given environment for specific agricultural activities. The key principle in the method is that land suitability for the growth of different plant species can be estimated from geo-referenced information on climate (e.g. temperature and rainfall regimes), soil (e.g. soil depth and pH) and terrain conditions (e.g. slope and aspect). The suitability model utilises a wider range of GIS-rules adapting the method from Kidd et al. (2015) and simplified "categorical" parameter ranges. To quantify land suitability for a given environment, the model translates the "biophysical attributes" into "suitability indexes" through crop-specific "suitability rules". For example, suitable growing conditions might occur between temperature ranges of 5 to 25°C for a given crop, with an optimum within a narrower range (e.g. 15 and 20°C). Suitability classes (4 classes were defined, they are well suited, suitable, marginally suited, and unsuitable) for different attribute-crop combinations can be estimated based on such suitability rules. The overall suitability index of a crop at a specific location takes value from the worst index (limiting factor) out of all the attributes index at that location. Suitability rules were either generated from the literature and through consultation with crop-specific experts. Each crop went through one or more iterations where experts would evaluate the resulting national map from current rules, identify areas where suitability was not sensible and suggest improvements to the rules to obtain expected spatial patterns.

The suitability rules are simplifications of biophysically sound principles that operate at a much lower scale of organisation (e.g. crop and plant physiological responses to the environment). Such simplification is necessary, given that suitability assessments are performed at the landscape level.

Reference: Kidd D, Webb M, Malone B, Minasny B, McBratney A. 2015. Digital soil assessment of agricultural suitability, versatility and capital in Tasmania, Australia. Geoderma Regional. 6: 7-21

 

Fitness for purpose / limitations

This table indicates whether the dataset is suitable for different types of questions at different scales.

Note: Users should carefully consider their purpose as this dataset may not be suitable.

Operational Absolute Relative Screening/scoping
Block/farm No No No Maybe
Multi-farms(5+) No No Maybe Yes
Catchment No Maybe Maybe Yes
National/regional No Yes Yes Yes
Caveat(s) Intended for national/regional use so limited accuracy especially at finer scales

Data and Resources