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the simple approach from French and Schulz with participatory modelling activities
using Agricultural Production Systems Simulator (APSIM) (Holzworth et al. 2014 )
to design step-wise intensification sequences that increase maize yields in small-
holder farming systems. Smallholder agricultural systems from Eastern and
Southern Africa are highly vulnerable to biotic and abiotic stresses and price fluc-
tuations, making the intensification of these mostly dryland cropping systems rather
challenging. Under these circumstances, increasing the productivity and resource-
use efficiency of agriculture is essential to achieve food secured households. Dimes
et al. ( 2015 ) found that small investments, e.g. low doses of N fertiliser or legume
residues, and improved weed control can dramatically increase water use efficien-
cies and yields by 40–150 % while maintaining yield stability across the seasonal
conditions.
These examples show how simple rules-of-thumb or conceptual frameworks
such as that from French and Schulz can help to answer questions about how one
can manage a crop or improve its genetics to increase yield in dryland cropping
systems and water-limited environments (Passioura and Angus 2010 ). However, the
design of farm practices, crop sequences, tactics and strategies that are more resil-
ient to change and better able to profit by emerging opportunities may require more
complicated simulation tools.
2.2 Field and Whole-Farm Level Dynamic Models
In the early 1970s, deWitt introduced the state variable approach as the basis for
simulation modelling, that the current state determines how the rates of change lead
to the next time step in the calculation of crop growth processes (Goudriaan and
vanLaar, 1994 ). This was probably the first step towards dynamic crop modelling.
Twenty years later, the Agricultural Production Systems Research Unit (APSRU) in
Australia used the existing understanding of eco-physiological processes to develop,
in collaboration with practitioners, a range of systems modelling tools with different
levels of complexity and scale i.e. field, farm, region, country. The two most- relevant
tools to this chapter are APSIM and its multi-field whole-farm configuration
(APSFarm).
2.3 Field Level: APSIM
APSIM is a modular modelling framework developed by APSRU in Australia
(www.apsim.info) to simulate the biophysical process in farming systems, particu-
larly where there is interest in the economic and ecological outcomes of manage-
ment practices in the face of climatic risk. APSIM’s structure consists of different
plant, soil and management modules which include a diverse range of crops, pas-
tures and trees, soil processes including water balance, N and P transformations,
D. Rodriguez et al.