Potato Crop Response to Genotype and Environment in a Subtropical Highland Agro-ecology

Molahlehi, Lebone; Steyn, J. M.; Haverkort, A. J.

doi:10.1007/s11540-013-9241-1

Cited by 15 publications

(7 citation statements)

References 26 publications

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“…Especially, Date and Kutchan that have relatively short seasons (Table 7) benefit more than average. With an estimated yield increase of 28% in potato associated with a CO 2 concentration of These findings are much in line with those of Haverkort et al (2013) who predicted similar yield increases in the partly rain-fed and irrigated summer crops in South Africa. This conclusion, however, deviates from a scientific report on the impact of global warming on agricultural productivity in Hokkaido (Agricultural Experimental Station 2011).…”

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

confidence: 88%

“…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growing season is not mentioned in ), A/P = ratio of actual potential yield a Figure 4 and Table 7 b Haverkort, A J. unpublished, the calculations were done for the present publication c Haverkort et al (2014) d Haverkort et al (2013) e Molahlehi et al (2013),* f Svubure et al (2015) the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%

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

confidence: 88%

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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“…Daily evapotranspiration (ET) was calculated by LINTUL as the product of reference evapotranspiration (ETo, according to Allen et al, 1998) and simulated canopy cover (Franke et al, 2011). The LINTUL model was previously calibrated and successfully used for other potato simulation studies in southern Africa (Franke et al, 2011, Molahlehi et al, 2013…”

Section: Methodsmentioning

confidence: 99%

Resource use efficiencies as indicators of ecological sustainability in potato production: A South African case study

Steyn

Franke

Waals

et al. 2016

Field Crops Research

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Potato, the most important vegetable crop in South Africa, is produced in many distinct geographical regions differing in climate, soils, production seasons and management practices and access to markets. These differences affect the amount of input resources required to produce potatoes as well as yields and crop value, and therefore the use efficiencies of land, water, nutrients, seed and energy. Resource use efficiencies affect the ecological and financial sustainability of potato production in this region, which has in general less favourable potato growing conditions than northwestern Europe and the U.S.A., where high resource use efficiencies are usually recorded. This study aimed to assess and benchmark South African potato production regions, representing a wide range of growing conditions, regarding their use of input resources and to identify resource-intensive practices, which may suggest inefficient use of inputs. Surveys were conducted in 2013 and 2014 by interviewing growers in all production regions, to provide data on resource use efficiencies. Quantitative modelling approaches were applied to calculate carbon footprints as a proxy of 2 energy use efficiency, potential crop yields and irrigation needs for each region. Variability in the gap between potential and actual yield was used to identify yield limiting factors. Actual yields achieved were on average 60% of the potential yield, suggesting fairly efficient use of available production factors. Water, seed and nutrient use efficiencies differed widely between and within regions and were not directly proportional to water requirements and yields achieved. Fertilizers (34%) and irrigation (30%) were the greatest contributors to energy use in potato crop production. Energy required to pump water was strongly related to the amount of irrigation applied, pumping depth and distance. Long distance travel of produce to retail points contributed substantially to energy use. Significant improvements in efficiencies are possible by improving management practices. Analysis of the variability in resource use efficiencies between farms and regions provided production sustainability indicators that can assist growers in identifying inefficient practices and yield limiting factors. These can be addressed through the use of decision support systems, such as irrigation scheduling tools, to improve resource use efficiencies and the sustainability of production, not only for the production efficiency of the specific study area, but also for the economic efficiency of potato production anywhere else.

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“…The growing demand for potato, coupled with the decreasing availability of fertile land for expansion, implies the need for better crop protection and management practices in order to improve crop yields [27]. Traditionally, crop growth models have been used to identify the effects of management options such as planting dates, population density, irrigation timing and frequency, as well as fertiliser applications in different environmental conditions on crop growth and yield [28,29]. In this context, crop models may prove useful for improving yield predictions for the potato processing industry [29].…”

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confidence: 99%

Potato Yield Prediction Using Machine Learning Techniques and Sentinel 2 Data

et al. 2019

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Traditional potato growth models evidence certain limitations, such as the cost of obtaining the input data required to run the models, the lack of spatial information in some instances, or the actual quality of input data. In order to address these issues, we develop a model to predict potato yield using satellite remote sensing. In an effort to offer a good predictive model that improves the state of the art on potato precision agriculture, we use images from the twin Sentinel 2 satellites (European Space Agency-Copernicus Programme) over three growing seasons, applying different machine learning models. First, we fitted nine machine learning algorithms with various pre-processing scenarios using variables from July, August and September based on the red, red-edge and infra-red bands of the spectrum. Second, we selected the best performing models and evaluated them against independent test data. Finally, we repeated the previous two steps using only variables corresponding to July and August. Our results showed that the feature selection step proved vital during data pre-processing in order to reduce multicollinearity among predictors. The Regression Quantile Lasso model (11.67% Root Mean Square Error, RMSE; R 2 = 0.88 and 9.18% Mean Absolute Error, MAE) and Leap Backwards model (10.94% RMSE, R 2 = 0.89 and 8.95% MAE) performed better when predictors with a correlation coefficient > 0.5 were removed from the dataset. In contrast, the Support Vector Machine Radial (svmRadial) performed better with no feature selection method (11.7% RMSE, R 2 = 0.93 and 8.64% MAE). In addition, we used a random forest model to predict potato yields in Castilla y León (Spain) 1-2 months prior to harvest, and obtained satisfactory results (11.16% RMSE, R 2 = 0.89 and 8.71% MAE). These results demonstrate the suitability of our models to predict potato yields in the region studied.The use of new technologies, such as satellite data, Geographic Information Systems (GIS) or Global Positioning Systems (GPS), can improve crop yield production and its quality [1], helping to secure food supply for the future as well as reducing the negative impacts resulting from agricultural practices [11]. More specifically, satellite remote sensing data has many applications in agriculture: soil property detection [12], crop type classification [13], crop yield forecast [14], crop health monitoring [15], soil moisture retrieval [16] or weather data assessment [17]. Remote sensing offers vast amounts of information which can be considered big data [18], and can help to improve crop modelling and decision-making. Big data has been described by Wolfert et al. [19] as "massive volumes of data with a wide variety that can be captured, analysed and used for decision-making", with said authors expecting big data to have a major impact on the agricultural sector. In order to improve the use of this data, given its size and variety, machine learning has emerged as an appropriate tool to identify rules and patterns in datasets [20], in addition to autonomously...

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Potato Crop Response to Genotype and Environment in a Subtropical Highland Agro-ecology

Cited by 15 publications

References 26 publications

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

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

Resource use efficiencies as indicators of ecological sustainability in potato production: A South African case study

Potato Yield Prediction Using Machine Learning Techniques and Sentinel 2 Data

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