Yield gap analysis and resource footprints of Irish potato production systems in Zimbabwe

Svubure, O.; Struik, P.C.; Haverkort, A. J.; Steyn, J. M.

doi:10.1016/j.fcr.2015.04.002

Cited by 32 publications

(42 citation statements)

References 24 publications

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“…Actual yields range from below 5 Mg fresh tubers ha −1 (median yield in Uganda: Gildemacher et al, 2009a) to well above 120 Mg fresh tubers ha −1 (actual yield under optimal conditions across years in Columbian Basin, USA: Kunkel and Campbell, 1987). The difference between potential yield and actual yield is called the yield gap (Svubure et al, 2015). Often the ratio between actual and potential yield is used to normalize the gap.…”

Section: Definitions Of Potential and Actual Yieldmentioning

confidence: 99%

Yield levels of potato crops: Recent achievements and future prospects

Haverkort

Struik

2015

Field Crops Research

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172

117

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Section: Definitions Of Potential and Actual Yieldmentioning

confidence: 99%

Yield levels of potato crops: Recent achievements and future prospects

Haverkort

Struik

2015

Field Crops Research

Self Cite

172

117

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“…Therefore, recent studies which applied the LINTUL-POTATO-DSS model were analysed and compared. Where certain data such as the length of the growing season is not mentioned in ), A/P = ratio of actual potential yield a Figure 4 and Table 7 b Haverkort, A J. unpublished, the calculations were done for the present publication c Haverkort et al (2014) d Haverkort et al (2013) e Molahlehi et al (2013),* f Svubure et al (2015) the publication cited, the original data on which the publication was based, were consulted. The results are shown in Table 10.…”

Section: Results Of the Ten Potato-growing Districts Of Hokkaidomentioning

confidence: 99%

Actual and Potential Yield Levels of Potato in Different Production Systems of Japan

2016

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Japan annually produces 2.4 million tons of potato on 79,700 ha. There are four cropping seasons stretching from 24°to 45°N: a winter crop at Okinawa, spring and autumn crops at Kyushu and southern Honshu, and a single summer crop at Hokkaido. Using the crop growth model LINTUL-POTATO-DSS, actual yields were compared with potential yields. The spring and summer crop yields of all prefectures varied from 10 to 43 t ha −1 and were 16 t ha −1 on average (not weighted by area); the actual/potential yield ratio varied between 0.21 and 0.63 and was 0.40 on average (not weighted). Actual and potential yields of the autumn crops were half those of the spring crops. Actual yields of the Hokkaido summer crops in 21 years decreased from 35 to 32 t ha −1 because of a change of varieties whereas potential yield remained stable at 56 t ha −1. Increased temperature would reduce the potential yield to 53 t ha −1 but would move it up to 60 t ha −1 by lengthening the growing season and to 77 t ha −1 when the CO 2 effect on growth is taken into account. The actual/potential yield ratio at Hokkaido was benchmarked against that of other growing regions in the world, showing that this area compares well with that of other efficient production systems in northern Europe and America.

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“…Crop growth models have previously been used to investigate the effects of management options such as planting dates, population density, irrigation timing and frequency, and fertiliser applications in different environmental conditions on crop growth and yield (Molahlehi et al 2013;Svubure et al 2015). Therefore, simulation models could potentially also be useful to help improve yield forecasts for the potato processing industry.…”

Section: Introductionmentioning

confidence: 99%

“…However, high yields do not necessarily lead to the best quality because recovery at the factory level mainly depends on high dry matter content (DMC), and uniform and relatively large tuber sizes. Both yield and quality of potato tubers are influenced by various yield determining, limiting and reducing factors (Haverkort and Struik 2015).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Yield and Tuber Size of Processing Potatoes in South Africa Using the LINTUL-Potato-DSS Model

et al. 2016

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The LINTUL-Potato-DSS model uses the linear relationship between radiation intercepted by the crop and radiation use efficiency (RUE), to calculate dry matter production. The model was developed into a yield forecasting system for processing potatoes based on long term and actual weather and crop data. The model outcome (Attainable yield, Yatt) was compared to actual yields (Yact) of a summer crop in South Africa and the ratio Yact:Yatt was used for forecasting yield in winter crops. Results showed that accurate forecasts (<20% variation between the actual and forecasted values) could be produced already early in the growing season and that for cultivar Innovator, actual and forecasted yields were well correlated (r = 0.797). Forecasted and observed yields at harvest were not significantly different at the 5% level, P = 0.637 (t-test). Forecasts of tuber number using LINTULPotato-DSS were not accurate in the present study and further research is needed on this aspect. It is concluded that the model is a valuable management tool that can be used to produce accurate 1 forecasts of tuber yield from as early as 8 weeks before final harvest. Since the model was tested with only one cultivar grown in three different growing regions of South Africa, further evaluation using different cultivars and localities is recommended.

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Yield gap analysis and resource footprints of Irish potato production systems in Zimbabwe

Cited by 32 publications

References 24 publications

Yield levels of potato crops: Recent achievements and future prospects

Yield levels of potato crops: Recent achievements and future prospects

Actual and Potential Yield Levels of Potato in Different Production Systems of Japan

Forecasting Yield and Tuber Size of Processing Potatoes in South Africa Using the LINTUL-Potato-DSS Model

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