Improving the prediction of potato productivity: APSIM-Potato model parameterization and evaluation in Tasmania, Australia

Borus, Dinah; Parsons, David; Boersma, M.; Brown, Hamish E.; Mohammed, CL

doi:10.21475/ajcs.18.12.01.pne570

Cited by 15 publications

(4 citation statements)

References 19 publications

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“…The simulation models forecast crop productivity by combining crop growth information including the physiological characteristics of plants, nutrient cycling, and environmental factors [ 19 ]. Over the past few decades, several simulation models have been developed for potato, such as POMOD [ 20 ], AQUACROP [ 21 ], APSIM-Potato [ 22 ], PotatoSoilWat [ 23 ], and LINTUL-Potato [ 24 ]. Although powerful, they typically require extensive crop-specific data as inputs, such as crop variety, management, and soil conditions, which are often difficult to obtain.…”

Section: Introductionmentioning

confidence: 99%

Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning

Sun

Liao

Zhang

et al. 2020

Sensors

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Potato is the largest non-cereal food crop in the world. Timely estimation of end-of-season tuber production using in-season information can inform sustainable agricultural management decisions that increase productivity while reducing impacts on the environment. Recently, unmanned aerial vehicles (UAVs) have become increasingly popular in precision agriculture due to their flexibility in data acquisition and improved spatial and spectral resolutions. In addition, compared with natural color and multispectral imagery, hyperspectral data can provide higher spectral fidelity which is important for modelling crop traits. In this study, we conducted end-of-season potato tuber yield and tuber set predictions using in-season UAV-based hyperspectral images and machine learning. Specifically, six mainstream machine learning models, i.e., ordinary least square (OLS), ridge regression, partial least square regression (PLSR), support vector regression (SVR), random forest (RF), and adaptive boosting (AdaBoost), were developed and compared across potato research plots with different irrigation rates at the University of Wisconsin Hancock Agricultural Research Station. Our results showed that the tuber set could be better predicted than the tuber yield, and using the multi-temporal hyperspectral data improved the model performance. Ridge achieved the best performance for predicting tuber yield (R2 = 0.63) while Ridge and PLSR had similar performance for predicting tuber set (R2 = 0.69). Our study demonstrated that hyperspectral imagery and machine learning have good potential to help potato growers efficiently manage their irrigation practices.

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Section: Introductionmentioning

confidence: 99%

Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning

Sun

Liao

Zhang

et al. 2020

Sensors

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“…There is a wide range of potato crop growth models in the literature such as SUBSTOR-Potato, LINTUL-Potato, SOLANUM, APSIMPotato, SPUDSIM, POMOD, SIMPOTATO or Potato Calculator [33][34][35]. However, most of these models have not been comprehensively tested to real field data and some have never even been used in a real application [36]. Their main limitations are the cost of obtaining the necessary input data required to run the models, the lack of spatial information in some instances, and the quality of the input data [37].…”

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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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“…Therefore, the RUE values under optimal conditions are likely to be even higher than the mean values observed in this study. Given the central role of RUE in many crop growth models (Griffin et al, 1993;Borus et al, 2018), a correct quantification of RUE and how it is affected by temperatures is essential for accurate crop growth simulations.…”

Section: Discussionmentioning

confidence: 99%

Photosynthesis rate, radiation and water use efficiencies of irrigated potato in a semi-arid climate using Eddy covariance techniques

Machakaire

Steyn²,

Franke³

2023

J. Agric. Sci.

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Understanding the photosynthetic behaviour, radiation use efficiency (RUE) and water use efficiency (WUE) of potatoes in response to diurnal and seasonal fluctuations in weather, is important for optimizing growth and production. The net ecosystem exchange (NEE) of carbon dioxide (CO2), is a parameter used to measure the balance between gross CO2 assimilation (also referred to as gross primary production (GPP)) and ecosystem respiration (Reco). The objectives of this study were (i) to quantify the NEE of potato grown in two contrasting agro-ecologies and partition NEE into Reco and GPP; (ii) to describe seasonal patterns in daily photosynthesis, RUE and WUE in potato; and (iii) to analyse the variability in photosynthesis rate, RUE and WUE as affected by weather and crop growth stage. We measured CO2 and water vapour fluxes using eddy covariance techniques in potato fields in two production regions of South Africa. The winter crop had higher mean RUE and WUE values, whereas the summer crop absorbed more CO2. RUE had a negative relationship with incident radiation. To optimize dry matter production, WUE and RUE, potato crops must be well established and develop a full canopy early during the season. Maintaining the crop for a longer period in the field has yield benefits, although they appear to decline over time due to reduced efficiencies as the crop matures. The RUE observed over the growing period in the present study was high, relative to findings by other field studies, despite relatively warm growing conditions that were likely sub-optimal for potato.

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Improving the prediction of potato productivity: APSIM-Potato model parameterization and evaluation in Tasmania, Australia

Cited by 15 publications

References 19 publications

Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning

Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning

Potato Yield Prediction Using Machine Learning Techniques and Sentinel 2 Data

Photosynthesis rate, radiation and water use efficiencies of irrigated potato in a semi-arid climate using Eddy covariance techniques

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