Water limitations on potato yield in Estonia assessed by crop modelling

Saue, Triin; Kadaja, J.

doi:10.1016/j.agrformet.2014.03.012

Cited by 18 publications

(12 citation statements)

References 35 publications

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“…Soil water content (SWC) is one of the most limiting factors for crop production by both water deficit and excess (Qin et al, 2013;Saue and Kadaja, 2014) and is especially relevant in semi-arid and sub-humid areas where irregular soil water content dynamics are frequent (Viola et al, 2012). Additionally, SWC in the uppermost layer, the topsoil (TSWC), which governs seedling establishment is a more limiting factor for crop yield (e.g.…”

Section: Introductionmentioning

confidence: 99%

Spatial patterns and stability of topsoil water content in a rainfed fallow cereal field and Calcisol-type soil

López‐Vicente

Quijano

Izquierdo

2015

Agricultural Water Management

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Topsoil water content (TSWC) is a key factor for crop establishment and subsequent production, runoff generation, soil detachment, and other soil processes. TSWC is one of the most variable soil properties due to the complexity of water fluxes in the unsaturated zone. The studies about TSWC in cultivated Calcisols are limited and almost inexistent under fallow treatment in rainfed cereal fields. We hypothesize that certain wetness characteristics (patterns, ranges and changes) of a soil portion remain over time. This study seeks to identify the spatial patterns of TSWC and their temporal stability in a Mediterranean fallow rainfed cereal field (1.6 ha, partial stubble retention, no weed growth allowed) and Haplic Calcisoltype soil. During 15 months (December 2009 to February 2011) and 25 field surveys measurements were made at 156 points (3 values per point) by using a frequency-domain probe. Values of TSWC varied significantly and four humidity periods were identified using

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Section: Introductionmentioning

confidence: 99%

Spatial patterns and stability of topsoil water content in a rainfed fallow cereal field and Calcisol-type soil

López‐Vicente

Quijano

Izquierdo

2015

Agricultural Water Management

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“…These classical potato models are mainly based on the response to nitrogen fertilizer [30], temperature, and daylight [31] or the incidence of solar radiation [32] and are often used to estimate yields during the growing season. 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].…”

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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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“…Water is usually the single most important limiting factor for the growth and productivity of plants (Inman-Bamber et al, 2012;Nielsen, 2003), and many areas worldwide have been facing increasingly severe water scarcity (De Fraiture et al, 2008;Zhang and Xia, 2009), especially the arid and semi-arid regions that are very sensitive to climate change and human activities (Rockström and Gordon, 2001;Saue and Kadaja, 2014;Wang et al, 2012). In the arid and semi-arid regions, extracting water to meet human needs jeopardizes the health of vital ecosystems (Postel, 2000), and the ecological water demand of natural ecosystems have largely been neglected, leading to tragic ecological consequences and decreasing the provision of a wealth of ecosystem services (Richter et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Multilevel modeling of NPP change and impacts of water resources in the Lower Heihe River Basin

Yan

Zhan

Jiang

et al. 2015

Physics and Chemistry of the Earth, Parts A/B/C

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a b s t r a c tNet primary productivity (NPP) lays the foundation for provision of various ecosystem services, and understanding the impacts of potential influencing factors on NPP is of great significance to formulating appropriate management measures to guarantee the sustainable provision of essential ecosystem services. This study analyzed the impacts of potential influencing factors on NPP in the lower Heihe River Basin, a typical arid and semi-arid region in China. Results of decomposition analysis suggested that the water availability played an important role in increasing NPP, with a contribution rate of 44.17%, and it is necessary to carry out some policies that can promote the water use efficiency to increase NPP under the background of climate change and intensified human activities. There are some uncertainties in the results of this study, but these results still can provide valuable reference information for the water resource management to increase the ecosystem service supply in the lower Heihe River Basin.

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Water limitations on potato yield in Estonia assessed by crop modelling

Cited by 18 publications

References 35 publications

Spatial patterns and stability of topsoil water content in a rainfed fallow cereal field and Calcisol-type soil

Spatial patterns and stability of topsoil water content in a rainfed fallow cereal field and Calcisol-type soil

Potato Yield Prediction Using Machine Learning Techniques and Sentinel 2 Data

Multilevel modeling of NPP change and impacts of water resources in the Lower Heihe River Basin

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