Crop Yields in Experiments and on Farms

Davidson, B. R.

doi:10.1038/194458a0

Cited by 13 publications

(8 citation statements)

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“…The observed yield gap between farm and experimental or modelled yields (Davidson 1962;Sadras and Angus 2006) is in contrast to the claims by Carberry et al (2002) that simulated yields from APSIM tested well against commercial yields obtained on farms. While Robertson et al (2000) demonstrated that APSIM closely simulated mungbean yields measured in commercial paddocks in Australia, simulations overpredicted actual harvested yield due to significant machineharvest losses.…”

Section: Introductioncontrasting

confidence: 53%

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Re-inventing model-based decision support with Australian dryland farmers. 3. Relevance of APSIM to commercial crops

Carberry¹,

Hochman²,

Hunt³

et al. 2009

Crop Pasture Sci.

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Crop simulation models relevant to real-world agriculture have been a rationale for model development over many years. However, as crop models are generally developed and tested against experimental data and with large systematic gaps often reported between experimental and farmer yields, the relevance of simulated yields to the commercial yields of field crops may be questioned. This is the third paper in a series which describes a substantial effort to deliver model-based decision support to Australian farmers. First, the performance of the cropping systems simulator, APSIM, in simulating commercial crop yields is reported across a range of field crops and agricultural regions. Second, how APSIM is used in gaining farmer credibility for their planning and decision making is described using actual case studies.Information was collated on APSIM performance in simulating the yields of over 700 commercial crops of barley, canola, chickpea, cotton, maize, mungbean, sorghum, sugarcane, and wheat monitored over the period 1992 to 2007 in all cropping regions of Australia. This evidence indicated that APSIM can predict the performance of commercial crops at a level close to that reported for its performance against experimental yields. Importantly, an essential requirement for simulating commercial yields across the Australian dryland cropping regions is to accurately describe the resources available to the crop being simulated, particularly soil water and nitrogen.Five case studies of using APSIM with farmers are described in order to demonstrate how model credibility was gained in the context of each circumstance. The proposed process for creating mutual understanding and credibility involved dealing with immediate questions of the involved farmers, contextualising the simulations to the specific situation in question, providing simulation outputs in an iterative process, and together reviewing the ensuing seasonal results against provided simulations.This paper is distinct from many other reports testing the performance and utility of cropping systems models. Here, the measured yields are from commercial crops not experimental plots and the described applications were from real-life situations identified by farmers. A key conclusion, from 17 years of effort, is the proven ability of APSIM to simulate yields from commercial crops provided soil properties are well characterised. Thus, the ambition of models being relevant to real-world agriculture is indeed attainable, at least in situations where biotic stresses are manageable.

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Section: Introductioncontrasting

confidence: 53%

“…Prior to the modelling era, discrepancy in yields between farm and experimental conditions was given prominence in Australia by Davidson (1962). He showed that mean weighted farm yields reached only 60% of experimental yields for wheat in Australia.…”

Section: Explaining On-farm Simulator Performancementioning

confidence: 97%

Re-inventing model-based decision support with Australian dryland farmers. 3. Relevance of APSIM to commercial crops

Carberry¹,

Hochman²,

Hunt³

et al. 2009

Crop Pasture Sci.

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“…The differences between trial and commercial yields observed in this study are in agreement with the findings of similar investigations (Bissessur et al, 2007;Davidson, 1962;Simmonds, 1980). The differences may be attributed to a number of factors: of Figure 2.…”

Section: Discussionsupporting

confidence: 92%

Evaluating Experimental and Commercial Yields: An Analysis of a Simple Correction Technique for Sugarcane in South Africa

Ramburan

2009

Ex. Agric.

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The objective of this study was to compare historical experimental and commercial yield data for sugarcane in order to determine the general level of disparity and assess the possibility of deriving a correction factor to adjust trial yields to realistic commercial levels. Over the 29-year comparison period, trial yields (fresh stalk weight) of sugarcane were significantly higher (p < 0.001) than commercial yields by approximately 30 t ha −1 . Trial and commercial yield data were used to derive a correction factor of 0.7, which can be used to successfully correct trial yields to equivalent commercial levels at both the national and regional level; however, at the local (mill supply area) level different correction factors were derived. The degree of correlation between experimental and commercial yields was found to be greater in areas of higher yield potential, and it was also established that off-station trials were more representative of the commercial potential than on-station trials. The correction factor of 0.7 can therefore be used as a tool to adjust experimental sugarcane yields to indicate likely commercial levels, thereby improving the confidence of growers in taking up recommendations for the adoption of new varieties derived from trial yields.

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“…It is the purpose of this article to demonstrate differences between experimental and commercial yields and to offer some explanations within the terms of classical production theory. We have previously made comparisons between farm and experimental yields for a number of crop and animal products, using a combination of cross-sectional and time-series data [1,2]. Only comparisons of crop yields based on time-series data will be referred to here, though the general conclusions can be applied also to animal products.…”

mentioning

confidence: 99%