Use of linear regression and correlation matrix in the evaluation of CERES3 (Maize)

Toit, A. S. du; Booysen, J.; Human, J. J.

doi:10.1080/02571862.1997.10635104

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…The weather analogue model chooses an analogue year to infill the missing data of the current season to enable yield forecasts (Du Toit et al, 2001). The weather analogue and a statistical procedure for model validation (Du Toit et al, 1997) were incorporated into DSSAT 4.0.…”

Section: Ceres and Cropgromentioning

confidence: 99%

“…It is essential to have knowledge about the accuracy of models in simulating the response of crop cultivars to local environmental and management conditions, before the models can be used in research and management applications. All the models reviewed in this paper have undergone thorough and widespread testing of various aspects using observations from local experiments Singels & De Jager, 1991;De Vos & Mallet, 1994;Schulze, 1995b;Du Toit et al, 1997;Hensley et al, 1997;Lumsden et al, 1999, Annandale et al, 2000Singels & Bezuidenhout, 2002;Jovanovic et al, 2004;Annandale et al, 2007). Phenological development, water balance, biomass and yield simulations, in particular, have received considerable attention.…”

Section: Calibration and Testing For Local Conditionsmentioning

confidence: 99%

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Modelling crop growth and crop water relations in South Africa: Past achievements and lessons for the future

Singels¹,

Annandale²,

Jager³

et al. 2010

South African Journal of Plant and Soil

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Mathematical simulation of crop growth and water relations has become indispensable to agricultural science and practice. A critical assessment of how modelling has contributed to the development of crop science and to the management of crop production and natural resources in South Africa (SA) over the past 25 years could give new perspectives on the benefits derived from modelling, the appropriateness of approaches employed and the best way forward. The initial objectives of the major SA modelling initiatives (ACRU, BEWAB, CANE-GRO, CERES, PUTU, SAPWAT, SWB) dictated the approaches that were followed and determined their impacts. Significant advances were made with regard to improved understanding of crop growth and water use and adapting models for local conditions such as dryland grain crop production under very low rainfall. Modelling provided invaluable support for strategic investigations into the impacts of climate change, land use and water use. Many of the models succeeded in providing much-needed information to improve tactical and operational management of irrigated and dryland agriculture. Some models have been (and are being) used operationally to forecast crop production (maize, wheat and sugar) and to monitor droughts in natural vegetation, adding value to the respective industries. Modelling has formed, in some cases, an integral part of tertiary education in crop science and hydrology. This should be strengthened to build more capacity to address the ever-increasing complexity of challenges in agriculture. The review identified factors that are crucial for modelling to maintain effective impacts on the science and practice of crop production and natural resource use. These were excellent scientific leadership, long term funding, effective collaboration between local and with international groups, expertise on local agronomy and high quality experimental data for model testing and adaptation. Future modelling efforts should explore opportunities to integrate information obtained from technologies such as remote sensing and genomics.

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Section: Ceres and Cropgromentioning

confidence: 99%

Section: Calibration and Testing For Local Conditionsmentioning

confidence: 99%

Modelling crop growth and crop water relations in South Africa: Past achievements and lessons for the future

Singels¹,

Annandale²,

Jager³

et al. 2010

South African Journal of Plant and Soil

View full text Add to dashboard Cite

show abstract

“…This is partly why crop models have occasionally been applied with inadequate validation. In cases where resources and time have permitted, experimental trials have been performed validating crop models in different locations in southern Africa (Dutoit et al 1997;Kamanga 2002;Dimes et al 2002, Singels et al 2010Mabhaudhi et al 2013). Some studies have gone a step further by engaging smallholder farmers to model actual on-farm conditions and yields (Thornton et al 1995;Ncube et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Crop model usefulness in drylands of southern Africa: an application of DSSAT

Zinyengere

Crespo

Hachigonta

et al. 2015

South African Journal of Plant and Soil

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International audienceCrop models are useful tools for simulating impacts of climate and agricultural practices on crops. Models have to demonstrate the ability to simulate actual crop growth response in particular environments before application. Data limitations in southern Africa frequently hinder adequate assessment of crop models before application. The DSSAT model was used to test the usefulness of crop models under data limited dryland conditions of southern Africa by validation using data from experimental trial reports and district-wide crop yield estimates. Two crops each were selected in three locations to represent varying cropping and physical conditions in southern Africa i.e. maize and sorghum (Mohale’s Hoek - Lesotho and Big Bend - Swaziland) and maize and groundnut (Lilongwe - Malawi). Results showed that DSSAT performs well in simulating crop yields obtained from experimental trials. District wide simulated mean crop yields were acceptable (relative difference ranged from -12.2 % to +2.36 %). However, the model’s capture of seasonal yield variation for some locations and crops was uncertain, due to climate extremes. It was concluded that satisfactory crop model testing before application is possible and that DSSAT crop models are useful even under data limited conditions

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Integrated assessment of climate change impacts on crop productivity and income of commercial maize farms in northeast South Africa

et al. 2020

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Use of linear regression and correlation matrix in the evaluation of CERES3 (Maize)

Cited by 9 publications

References 14 publications

Modelling crop growth and crop water relations in South Africa: Past achievements and lessons for the future

Modelling crop growth and crop water relations in South Africa: Past achievements and lessons for the future

Crop model usefulness in drylands of southern Africa: an application of DSSAT

Integrated assessment of climate change impacts on crop productivity and income of commercial maize farms in northeast South Africa

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