44 M. van den Berg et al. Agriculture, Ecosystems and Environment 81 2000 43–55
data for water balance calculations of crop models may cause substantial deviation in computed yield potentials. Results suggest that future research on crop water relations in south-east Brazil should give special attention to in situ determination
of water withdrawal by roots as related to soil Al concentration. © 2000 Elsevier Science B.V. All rights reserved.
Keywords: Error analysis; Ferralsols; Crop modelling; Soil variability; Water availability; Aluminium toxicity
1. Introduction
Crop growth simulation models are increasingly being used in combination with GIS as practical tools
for land-use assessment at regional or national lev- els e.g. de Koning and van Diepen, 1992; Rötter
and Dreiser, 1994. For these purposes, simplified so- called summary models with moderate need for input
data seem most appropriate Dumanski and Onofrei, 1989; Driessen, 1997. Transfer functions may be used
to estimate parameter values or input data that are diffi- cult or expensive to obtain, from easily available data.
Experience shows that modelled crop yield poten- tials are generally considerably higher than actual
yields; ideally, the yield gaps represent the conse- quences of all yield limiting and reducing factors
e.g. nutrient shortage, weeds, pests, diseases that are not considered by the model Boote et al., 1996; van
Diepen et al., 1998. However, there are additional reasons for differences between calculated yield po-
tentials and actual yield records: 1 inadequate basic input data including parameter values andor trans-
fer functions to generate these data; 2 errors in the model and 3 errorsuncertainties in actual yield data.
These factors may cause scatter, masking the true yield gap, or cause biased yield gap estimates. These
are particularly disturbing in analysis in develop- ing countries where land-use assessments are highly
relevant, basic data are scarce and data-sets for cali- bration and validation to correct for bias are virtually
absent. Quantified indications of the impact of differ- ent types of error on model outcomes are needed to
guide further research and to identify priorities in data collection and to judge the appropriateness of specific
types of models for specific types of assessment.
Sugarcane growers in São Paulo State, Brazil consider water availability to be the major cause
of inter-annual yield variation and yield differences on different soils. Soils used for sugarcane are
mostly strongly weathered, red and yellow Ferralsols, Acrisols, Lixisols and Ferralic Arenosols. Farmers ap-
ply modern management but irrigation is not normally applied because the average annual rainfall sum ex-
ceeds 1300 mm. Major yield reductions are blamed on so called veranicos: dry spells of more than 2 weeks
during the hot rainy season November–March. The relatively dry and cool months of May through
September form the season for cane ripening and harvest and, obviously, also for germination and early
establishment of a following ‘ratoon’ crop. Additional factors conditioning water availability are 1 water
retention characteristics of the soil and 2 limited rooting caused by acid subsurface soil with large Al
saturation fraction of effective cation exchange ca- pacity occupied by Al
3+
. Layers with 50–60 Al
3+
saturation are often considered to form a chemical barrier to the roots of most crops Furlani et al.,
1991; Foy, 1992. Crop growth modelling exercises to identify yield gaps under practical conditions of the
region have to cope with many sources of error and uncertainties. This paper addresses possible errors in
actual yield estimates from a sugarcane estate in the surroundings of Araras São Paulo, and the follow-
ing factors affecting water availability assessment: 1 input data on soil-water relations obtained from
reference profiles, whereas van den Berg and Oliveira 2000a showed that many apparently homogeneous
soils in the region are in fact quite heterogeneous; 2 maximum rootable soil depth RDM, cm, limited by
Al
3+
saturation, estimated by a crude method for the same reference profiles and 3 strongly simplified
methods to describe the relation between soil-water status and crop transpiration.
The objectives of this study are 1 to assess the impacts of the above mentioned errorsuncertainties
on calculated sugarcane yield potentials in relation to the inferred yield gaps and 2 to demonstrate that
uncertainty propagation analyses should be a stan- dard practice in quantified land-use system assess-
ments.
M. van den Berg et al. Agriculture, Ecosystems and Environment 81 2000 43–55 45
Results of the assessment must be interpreted in re- lation to the studied situation and the crop model. Un-
certainties that strongly affect results certainly need consideration: more or better basic data are necessary
or the model needs improvement, or both. Uncertain- ties that contribute little to the variation of calculated
results suggest that, for the studied conditions, there is little need for better data. However, the possibility
that the factor considered is not well accounted for in the model should not be excluded, e.g. a model that
does not take account of possible chemical root barri- ers will obviously not be sensitive to their occurrence.
Note further that this assessment considers only part of all possible errors, i.e. the results indicate a mini-
mum level of uncertainty.
2. Methods
