Global crop calendars of maize and wheat in the framework of the WorldCereal project

Franch, B.; Cintas, Juanma; Becker‐Reshef, Inbal; Sánchez-Torres, María José; Roger, J.; Skakun, Sergii; Sobrino, José Antonio Sánchez; Tricht, Kristof Van; Degerickx, Jeroen; Gilliams, Sven; Koetz, Benjamin; Szantoi, Zoltan; Whitcraft, A. K.

doi:10.1080/15481603.2022.2079273

Cited by 15 publications

(12 citation statements)

References 61 publications

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“…Crop calendar data showed that the end of the season (EOS) for grain like winter wheat in mid-Europe is in July and August 30 , 31 . So the first investigation period will start connected with the end of season in July (Date: 01.07.2019) (Sec.…”

Section: Methodsmentioning

confidence: 99%

“…Crop calendar data showed that the end of the season (EOS) for grain like winter wheat in mid-Europe is in July and August. 30,31 With the start of sowing new crops on the overwintering stubble fields in March, tilling, and freezing of the herbs in January and February the observation period for stubble ended 15 (Date: 31.03.2020) (Sec. 2.4.1; Table 2, Column: Period of time).…”

Section: Overwintering Stubble Fields As Study Objectmentioning

confidence: 99%

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Mapping overwintering grain stubbles using machine-learning methods and image compositions for common agriculture policy-control and water framework directive connected activities

Goihl

2023

J. Appl. Rem. Sens.

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By leaving grain stubble on the field over the winter, farmers can actively contribute to nature conservation. Animals receive food and living space in these areas, endangered herbs too, but at the same time, it is reducing the input of substances into water and function as covered ground to protect the fertile soil from erosion. Remote sensing can deliver information on this type of land cover, which is also important for the agricultural administration. The farmer can be compensated by the government to let stubbles on the fields. The results of this study show that there is a high potential to detect overwintering stubbles fields area for common agriculture policy (CAP)-control to verify agricultural funding with Support Vector Machine (96.5%) by combining remote sensing classification methods with geodata analysis techniques using geoinformation systems (GIS), negative buffering (-20 m), and using threshold settings for correct classified pixel per field. These results give occasion for optimism for the further processing of this land cover by using it as an application for the government to support CAP-and water framework directive-activities.

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Section: Methodsmentioning

confidence: 99%

Section: Overwintering Stubble Fields As Study Objectmentioning

confidence: 99%

Mapping overwintering grain stubbles using machine-learning methods and image compositions for common agriculture policy-control and water framework directive connected activities

Goihl

2023

J. Appl. Rem. Sens.

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“…Wheat plays a crucial role in the agricultural economy of the country [ 47 ]. The wheat growth cycle in the region prevails from April to November, with slight variations in its summer and winter characteristics [ 61 ]. Irrigated wheat is planted in summer rainfall regions in eastern states from mid-May to the end of July [ 62 ], while in winter rain areas such as WES-C, it is planted from mid-April to mid-June [ 63 ].…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Irrigated wheat is planted in summer rainfall regions in eastern states from mid-May to the end of July [62], while in winter rain areas such as WES-C, it is planted from mid-April to mid-June [63]. The harvesting period of wheat in the region runs from October to November [61]. Between 500,000 and 900,000 hectares of cereal crop is cultivated per annum, with a mean yearly output of 1.3 to 2.4 million tons between 2000 and 2021.…”

Section: Data Collectionmentioning

confidence: 99%

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Meteorological Drought Variability and Its Impact on Wheat Yields across South Africa

Nxumalo

Bashir

Alsafadi

et al. 2022

IJERPH

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Drought is one of the natural hazards that have negatively affected the agricultural sector worldwide. The aims of this study were to track drought characteristics (duration (DD), severity (DS), and frequency (DF)) in South Africa between 2002 and 2021 and to evaluate its impact on wheat production. Climate data were collected from the South African Weather Service (SAWS) along with wheat yield data from the Department of Agriculture, Forestry and Fisheries (2002–2021). The standard precipitation index (SPI) was calculated on 3-, 6-, 9-, and 12-month time scales, and the trend was then tracked using the Mann–Kendall (MK) test. To signify the climatic effects on crop yield, the standardized yield residual series (SYRS) was computed along with the crop-drought resilience factor (CR) on a provincial scale (2002–2021). The output of the SPI analysis for 32 stations covering all of South Africa indicates a drought tendency across the country. On a regional scale, western coastal provinces (WES-C and NR-C) have been more vulnerable to meteorological droughts over the past 20 years. Positive correlation results between SYRS and wheat yield indicate that the WES-C province was highly influenced by drought during all stages of wheat growth (Apr–Nov). Historical drought spells in 2003, 2009, and 2010 with low CR = 0.64 caused the province to be highly impacted by the negative impacts of droughts on yield loss. Overall, drought events have historically impacted the western part of the country and dominated in the coastal area. Thus, mitigation plans should be commenced, and priority should be given to this region. These findings can assist policymakers in budgeting for irrigation demand in rainfed agricultural regions.

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Crop yield estimation uncertainties at the regional scale for Saxony, Germany

Goihl

2024

Agronomy Journal

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In times of climate change and global population growth, agricultural yield forecasts play an increasingly important role. For example, predicting yields as early as possible in the event of a drought is crucial for decision‐makers in politics, government, and business. The aim of this study was to provide precise yield predictions at agricultural regions as early as possible with a minimum amount of weather data. Random forest models were used for this purpose. Although more than 290,000 datasets were available for analysis, all models tended to be heavily overfitting, which can be explained by the strong fragmentation of the input data by crop, region, and prediction time. The models reacted very differently to unknown datasets. It was found that the regionally trained models achieved lower (≥10%) relative root mean square errors (RRMSEs) than the supra‐regionally trained models. Rapeseed and barley achieved good predictions. Wheat had good potential, too. Corn, potatoes, and sugar beet achieved often too high RRMSEs. The results showed that targeted model selection for each region and an extension of the training time series could enable very good regional yield forecasts for rapeseed and cereals in the future.

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Global crop calendars of maize and wheat in the framework of the WorldCereal project

Cited by 15 publications

References 61 publications

Mapping overwintering grain stubbles using machine-learning methods and image compositions for common agriculture policy-control and water framework directive connected activities

Mapping overwintering grain stubbles using machine-learning methods and image compositions for common agriculture policy-control and water framework directive connected activities

Meteorological Drought Variability and Its Impact on Wheat Yields across South Africa

Crop yield estimation uncertainties at the regional scale for Saxony, Germany

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