AgriYieldz: The Wheat Crop Product v2 (File Download)

AgriYieldz: The Wheat Crop Production Estimator AgriYieldz provides information about the historical yield potential of a paddock for every year from 1990 to 2023 (33) years. To estimate potential yield, it uses best available soil information, real rainfall data, and assumes the paddock was sown to wheat, every year. It also assumes no other constraints such as suffering from a lack of nitrogen, or impacts of weeds, disease or frost.

AgriYieldz is generated on an 11km2 climate grid, for each soil in the grid. A simulation is performed for each combination of soil and climate. Wheat yields are calculated using soil types from the ASRIS national soil grid of Australia. Historical climate data is provided by the Bureau of Meteorology. Yields from six different cultivars are simulated. Cultivars represent the variations in phenology and cultivar options include Bolac, Braewood, H45, Illabo, Ouyen, Scepter and Sunstate.  The simulation assumes wheat was grown every year, for the last 30 years. With the APSIM crop model, management was set so that the crop was grown with unlimited nitrogen and was free from weeds and disease. Two yields are provided, one where frost and heat damage are calculated, and another where these were excluded from the estimate of yield potential.

How can I use this data?

For a given field, the data generated, allows people to:

  • Estimate the long term average wheat yield potential.
  • Estimate the degree of frost or heat damage for a field.
  • Understand the variability in wheat yield potential, and the range of yield potentials for the last 30 years.
  • Gauge the short term trajectory in wheat yield potential for the last 30 years, (e.g. is my paddocks yield potential trending down or up?)
  • Compare the theoretical yield potential of multiple paddocks, when information is extracted for multiple grid locations. The variability in production between paddocks or regions can also be evaluated.

The data is provided as a vector, with an estimated yield for each of the last 30 years. The dataset is a set of point locations, with dimensions {lat, lon, year}.

Each simulation has the following variables, outlined in table 1.

Table 1. Variables in the AgriYieldz data product.

YearNumber of Frost Events

APSoil (The APSIM soil)

Number of Heat Events

Area

Fallow Rain

Plant Available Water Capacity

In Crop Rainfall

Soil Type

Sowing Plant Available Water

Cultivar

Harvest Plant Available Water
Sowing Date and DayTranspiration
Harvest DateWet Yield (t/ha)
Cumulative Frost (Damage)Yield (t/ha)
Cumulative Heat (Damage)Frost Heat Yield (t/ha, with frost and heat damage included)

Emerge Days After Sowing

Biomass (t/ha)
Flowering Days After Sowing

Grain Number

Maturity Days After Sowing

Grain Protein

Harvest Days After Sowing

Grain Size

Crop Survived (yes/no)

Scientific literature

Holzworth, Dean, N. I. Huth, J. Fainges, H. Brown, E. Zurcher, R. Cichota, S. Verrall, N. I. Herrmann, B. Zheng, and V. Snow. ‘APSIM Next Generation: Overcoming Challenges in Modernising a Farming Systems Model’. Environmental Modelling & Software 103 (1 May 2018): 43–51. https://doi.org/10.1016/j.envsoft.2018.02.002.

Searle, R, Hochman, Z, Horan H, Steinberg D (2021) A Method for Assessing the Spatial Drought Risk of Winter Cereal Cropping in Australia Using Digital Soil Mapping and Deterministic Crop Modelling. In Review.

AgriYieldz Australia v2

$4,500.00 excl tax

AgriYieldz NSW v2

$1,250.00 excl tax

AgriYieldz QLD v2

$1,250.00 excl tax

AgriYieldz SA v2

$1,250.00 excl tax

AgriYieldz TAS v2

$1,250.00 excl tax

AgriYieldz VIC v2

$1,250.00 excl tax

AgriYieldz WA v2

$1,250.00 excl tax