240
increase food production by 70 %, or nearly 100 % in low-income countries, by
2050 to cope with the expected increases in population and food demand.
Dryland agricultural systems are the predominant food production system in the
world. Of the total global land area of about 13.3 billion ha, 12 % is used for cultiva-
tion of agricultural crops, most of which (80 %, or about 1.3 billion ha) is under
dryland conditions (FAO 2015 ). Rainfed agriculture produces about 60 % of global
crop output across the highlands, dry tropics, humid tropics, subtropics and temper-
ate regions of the world (FAO 2011 ).
Gaps in achievable yield are still significant in dryland agricultural systems;
present performance is reportedly up to 50 % below (i.e. yield gap) the achievable
yield^1 , suggesting that there is ample room for improvement (Lobell et al. 2009 ). A
more detailed analysis, including country and crop-specific yield gaps that take into
account the constraints of existing cropping systems, can be found at the Global
Yield Gap Atlas website, together with a common methodology for its calculation.
High productivity and consequent small yield and production gaps are the result
of optimum combinations of crops, cultivars or hybrids a management variable that
best fit a particular environment, as well as how the limited resources e.g. labour,
land, finances are allocated across enterprises and fields at the whole-farm level
(Calviño and Monzon 2009 ; Rodriguez et al. 2009 , 2011 ; Power et al. 2011 ). As a
result, focusing on individual crop yields is necessary but insufficient for several
reasons. Firstly, large improvements in productivity are likely from interventions at
scales beyond the crop, the selection of the cultivar or the management of a particu-
lar field. Secondly, changes in individual crop yields may not reflect the fact that
farmers manage their farms and resources to satisfy competing objectives: liveli-
hoods, returns, lifestyle and environmental or societal outputs, rather than just
increasing crop yields. It is only when the analysis is performed at the whole farm
level that changes in one enterprise at any point in time will limit options spatially
across the farm e.g., due to land, labour or machinery constraints, and temporally
across seasons e.g., due to follow-on implications on soil water and nutrient avail-
ability, or the need for pest or disease breaks between successive crops (Sadras et al.
2009 ; Rodriguez et al. 2011 ; Power et al. 2011 ). This chapter aims to scale up the
discussion and analysis of farming systems research in dryland systems to the level
that farmers think and make decisions, this is the whole farm, or the farm business.
The boundaries of the system then become the farm property rather than the field,
and key resources are the availability of land of different use, type and fertility, the
availability of labour, water for irrigation, cash, machinery and management
resources and skills. It is at this level of analysis when agricultural systems model-
ling tools are most useful, to quantify benefits and trade-offs from alternative
(^1) The yield ‘achieved’ from applying optimum agronomic management under rainfed conditions,
also called water-limited yield. Water-limited yield can be calculated using crop simulation models
assuming optimum or recommended sowing dates, planting densities and cultivars. An important
limitation of this concept is that farmers tend to maximize profits from the entire farm business
rather than from individual enterprises.
D. Rodriguez et al.