Precision Agriculture (PA) strives towards holistic production and environmental
management. A fundamental research challenge is the continuous expansion of ideas
about how PA can contribute to sustainable agriculture. Some associated pragmatic
research challenges include quantification of spatio-temporal variation of crop yield; crop
growth simulation modelling within a PA context and; evaluating long-term financial and
environmental outcomes from site-specific crop management (SSCM).
In Chapter 1 literature about managing whole farms with a mind towards sustainability
was reviewed. Alternative agricultural systems and concepts including systems thinking,
agro-ecology, mosaic farming and PA were investigated. With respect to environmental
outcomes it was found that PA research is relatively immature. There is scope to
thoroughly evaluate PA from a long-term, whole-farm environmental and financial
perspective. Comparatively, the emphasis of PA research on managing spatial variability
offers promising and innovative ways forward, particularly in terms of designing new
farming systems. It was found that using crop growth simulation modelling in a PA
context is potentially very useful. Modelling high-resolution spatial and temporal
variability with current simulation models poses a number of immediate research issues.
This research focused on three whole farms located in Australia that grow predominantly
grains without irrigation. These study sites represent three important grain growing
regions within Australia. These are northern NSW, north-east Victoria and South
Australia. Note-worthy environmental and climatic differences between these regions
such as rainfall timing, soil type and topographic features were outlined in Chapter 2.
When considering adoption of SSCM, it is essential to understand the impact of temporal
variation on the potential value of managing spatial variation. Quantifying spatiotemporal
variation of crop yield serves this purpose; however, this is a conceptually and
practically challenging undertaking. A small number of previous studies have found that
the magnitude of temporal variation far exceeds that of spatial variation. Chapter 3 of this
thesis dealt with existing and new approaches quantifying the relationship between spatial
and temporal variability in crop yield. It was found that using pseudo cross variography
to obtain spatial and temporal variation ‘equivalents’ is a promising approach to
quantitatively comparing spatial and temporal variation. The results from this research
indicate that more data in the temporal dimension is required to enable thorough analysis
using this approach. This is particularly relevant when questioning the suitability of
Crop growth simulation modelling offers PA a number of benefits such as the ability to
simulate a considerable volume of data in the temporal dimension. A dominant challenge
recognised within the PA/modelling literature is the mismatch between the spatial
resolution of point-based model output (and therefore input) and the spatial resolution of
information demanded by PA. This culminates into questions about the conceptual model
underpinning the simulation model and the practicality of using point-based models to
simulate spatial variability.
The ability of point-based models to simulate appropriate spatial and temporal variability
of crop yield and the importance of soil available water capacity (AWC) for these
simulations were investigated in Chapter 4. The results indicated that simulated spatial
variation is low compared to some previously reported spatial variability of real yield
data for some climate years. It was found that the structure of spatial yield variation was
directly related to the structure of the AWC and interactions between AWC and climate.
It is apparent that varying AWC spatially is a reasonable starting point for modelling
spatial variation of crop yield. A trade-off between capturing adequate spatio-temporal
variation of crop yield and the inclusion of realistically obtainable model inputs is
A number of practical solutions to model parameterisation for PA purposes are identified
in the literature. A popular approach is to minimise the number of simulations required.
Another approach that enables modelling at every desired point across a study area
involves taking advantage of high-resolution yield information from a number of years to
estimate site-specific soil properties with the inverse use of a crop growth simulation
model. Inverse meta-modelling was undertaken in Chapter 5 to estimate AWC on 10-
metre grids across each of the study farms. This proved to be an efficient approach to
obtaining high-resolution AWC information at the spatial extent of whole farms. The
AWC estimates proved useful for yield prediction using simple linear regression as
opposed to application within a complex crop growth simulation model.
The ability of point-based models to simulate spatial variation was re-visited in Chapter 6
with respect to the exclusion of lateral water movement. The addition of a topographic
component into the simple point-based yield prediction models substantially improved
yield predictions. The value of these additions was interpreted using coefficients of
determination and comparing variograms for each of the yield prediction components. A
result consistent with the preceding chapter is the importance of further validating the
yield prediction models with further yield data when it becomes available.
Finally, some whole-farm management scenarios using SSCM were synthesised in
Chapter 7. A framework that enables evaluation of the long-term (50 years) farm
outcomes soil carbon sequestration, nitrogen leaching and crop yield was established. The
suitability of SSCM across whole-farms over the long term was investigated and it was
found that the suitability of SSCM is confined to certain fields. This analysis also enabled
identification of parts of the farms that are the least financially and environmentally
viable. SSCM in conjunction with other PA management strategies is identified as a
promising approach to long-term and whole-farm integrated management.