scholarly journals Machine learning methods to assess the effects of a non-linear damage spectrum taking into account soil moisture on winter wheat yields in Germany

2021 ◽  
Author(s):  
Michael Peichl ◽  
Stephan Thober ◽  
Luis Samaniego ◽  
Bernd Hansjürgens ◽  
Andreas Marx
Keyword(s):  
Soil Moisture ◽  
Winter Wheat ◽  
Agricultural Production ◽  
Detrimental Effect ◽  
Crop Yields ◽  
Soil Column ◽  
Machine Learning Methods ◽  
Non Linear ◽  
Average Marginal Effects ◽  
Drought Damage

Abstract. Agricultural production is highly dependent on the weather. The mechanisms of action are complex and interwoven, making it difficult to identify relevant management and adaptation options. The present study uses random forests to investigate such highly non-linear systems for predicting yield anomalies in winter wheat at district level in Germany. In order to take into account sub-seasonality, monthly features are used that explicitly take soil moisture into account in addition to extreme meteorological events. Clustering is used to show spatially different damage potentials, such as a higher susceptibility to drought damage from April to July in eastern Germany compared to the rest of the country. The variable that explains most differences is soil moisture in March, where higher soil moisture has a detrimental effect on crop yields. In general, soil moisture explains more yield variations than the meteorological variables, while the top 25 cm of soil moisture is a better yield predictor than the total soil column. The approach has proven to be suitable to explain historical extreme yield anomalies for years with exceptionally high losses (2003, 2018) and gains (2014) and the spatial distribution of these anomalies. The highest test R-square is about 0.70. Furthermore, the sensitivity of yield variations to soil moisture and extreme meteorological conditions, as shown by the visualisation of average marginal effects, contributes to the promotion of targeted decision support systems.

scholarly journals Machine-learning methods to assess the effects of a non-linear damage spectrum taking into account soil moisture on winter wheat yields in Germany

2021 ◽  
Vol 25 (12) ◽  
pp. 6523-6545
Author(s):  
Michael Peichl ◽  
Stephan Thober ◽  
Luis Samaniego ◽  
Bernd Hansjürgens ◽  
Andreas Marx
Keyword(s):  
Soil Moisture ◽  
Winter Wheat ◽  
Agricultural Production ◽  
Detrimental Effect ◽  
Crop Yields ◽  
Machine Learning Methods ◽  
Heat Effects ◽  
Non Linear ◽  
Average Marginal Effects ◽  
Drought Damage

Abstract. Agricultural production is highly dependent on the weather. The mechanisms of action are complex and interwoven, making it difficult to identify relevant management and adaptation options. The present study uses random forests to investigate such highly non-linear systems for predicting yield anomalies in winter wheat at district levels in Germany. In order to take into account sub-seasonality, monthly features are used that explicitly take soil moisture into account in addition to extreme meteorological events. Clustering is used to show spatially different damage potentials, such as a higher susceptibility to drought damage from May to July in eastern Germany compared to the rest of the country. In addition, relevant heat effects are not detected if the clusters are not sufficiently defined. The variable with the highest importance is soil moisture in March, where higher soil moisture has a detrimental effect on crop yields. In general, soil moisture explains more yield variations than the meteorological variables. The approach has proven to be suitable for explaining historical extreme yield anomalies for years with exceptionally high losses (2003, 2018) and gains (2014) and the spatial distribution of these anomalies. The highest test R-squared (R2) is about 0.68. Furthermore, the sensitivity of yield variations to soil moisture and extreme meteorological conditions, as shown by the visualization of average marginal effects, contributes to the promotion of targeted decision support systems.

scholarly journals Extreme Dry Years in the 21st Century at the Level of the Agricultural Areas of Muntenia, Romania

2018 ◽  
Vol 1 (1) ◽  
pp. 101-109 ◽  
Author(s):  
Oana-Alexandra Oprea ◽  
Elena Mateescu ◽  
Anda Barbu ◽  
Rodica Tudor
Keyword(s):  
Soil Moisture ◽  
Winter Wheat ◽  
Air Temperature ◽  
21St Century ◽  
Agricultural Production ◽  
Natural Phenomenon ◽  
Rainfall Regime ◽  
Limiting Factor ◽  
Climate Data ◽  
Agricultural Areas

Abstract The global climatic changes consisting of the increased in the average air temperature and changes in the rainfall regime have led in the last decades to the extension of the agricultural areas affected by the drought phenomenon, both worldwide and in Romania. During the last half century, the drought and the phenomena associated with it, namely aridization and desertification, are a major problem for mankind. The limiting factor affecting field crops on the largest surface is the drought, the extent and intensity of this type of risk causing annual reduction of agricultural production by at least 30-50%. Drought represents the natural phenomenon determined by the amounts of precipitations below the normal values. The absence of rainfall is due to the predominance of the anti-cyclonic type. The most frequent phenomena occur in the extra-Carpathian agricultural regions of southern and south-eastern Romania Muntenia is located in the drought-sensitive area, where the influx of continental anti-cyclones is higher. Although this phenomena is possible in all seasons and in all agricultural areas, it doesn’t occur simultaneously and doesn’t have the same intensity. In the 21st century, the agricultural years 2001-2002, 2002-2003, 2006-2007, 2011-2012 and 2014-2015 are included in the list of the most recent years in terms of rainfall quantities, the heat units recorded in the warm season, as well as the soil moisture reserve available to winter wheat and maize plants during maximum water consumption. The objective of this paper is to highlight the correlation between the pluviometric regime analyzed during periods of maximum consumption of water from winter wheat and maize crops, the phenomenon of "heat" and the soil moisture reserve. The analysis of these specific indices helped us characterize the mentioned agricultural years, in the context of analysing the phenomenon of pedological drought with an impact in agriculture in Muntenia Region. An important element in the development of agricultural management strategies is to improve scientific knowledge and capacities to better manage climate variability by examining climate data and risks and opportunities analysis. The decrease in production of winter wheat and maize wheat crops occurs in extreme dry agricultural years due to the shortening of the vegetation season as a result of the increase in air temperature and water stress during the period of accumulation of the dry matter in the grain (the filling phase grain) caused by the reduction of precipitation amounts. Drought periods are increasingly common in Romania and are a major problem for agriculture with high impact on the agricultural production.

scholarly journals INVERSION OF FARMLAND SOIL MOISTURE IN LARGE REGION BASED ON MODIFIED VEGETATION INDEX

2018 ◽  
Vol XLII-3 ◽  
pp. 1739-1746
Author(s):  
J. X. Wang ◽  
B. S. Yu ◽  
G. Z. Zhang ◽  
G. C. Zhao ◽  
S. D. He ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Winter Wheat ◽  
Surface Temperature ◽  
Land Surface ◽  
Agricultural Production ◽  
Vegetation Index ◽  
Reference Value ◽  
Growth Period ◽  
Henan Province ◽  
Easy Access

Soil moisture is an important parameter for agricultural production. Efficient and accurate monitoring of soil moisture is an important link to ensure the safety of agricultural production. Remote sensing technology has been widely used in agricultural moisture monitoring because of its timeliness, cyclicality, dynamic tracking of changes in things, easy access to data, and extensive monitoring. Vegetation index and surface temperature are important parameters for moisture monitoring. Based on NDVI, this paper introduces land surface temperature and average temperature for optimization. This article takes the soil moisture in winter wheat growing area in Henan Province as the research object, dividing Henan Province into three main regions producing winter wheat and dividing the growth period of winter wheat into the early, middle and late stages on the basis of phenological characteristics and regional characteristics. Introducing appropriate correction factor during the corresponding growth period of winter wheat, correcting the vegetation index in the corresponding area, this paper establishes regression models of soil moisture on NDVI and soil moisture on modified NDVI based on correlation analysis and compare models. It shows that modified NDVI is more suitable as a indicator of soil moisture because of the better correlation between soil moisture and modified NDVI and the higher prediction accuracy of the regression model of soil moisture on modified NDVI. The research in this paper has certain reference value for winter wheat farmland management and decision-making.

Effects of Plastic Mulching Modes on Soil Moisture and Grain Yield in Dryland Winter Wheat

2015 ◽  
Vol 41 (5) ◽  
pp. 787 ◽  
Author(s):  
Shou-Xi CHAI ◽  
Chang-Gang YANG ◽  
Shu-Fang ZHANG ◽  
Heng-Hong CHEN ◽  
Lei CHANG
Keyword(s):  
Soil Moisture ◽  
Winter Wheat ◽  
Grain Yield ◽  
Plastic Mulching

scholarly journals Soil pH, Soil Organic Matter, and Crop Yields in Winter Wheat-Summer Fallow Systems

2017 ◽  
Vol 109 (2) ◽  
pp. 706-717 ◽  
Author(s):  
Rajan Ghimire ◽  
Stephen Machado ◽  
Prakriti Bista
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Winter Wheat ◽  
Soil Ph ◽  
Crop Yields ◽  
Fallow Systems ◽  
Summer Fallow

HARMFUL EFFECT OF WORKED-DOWN WINTER WHEAT ON SPRING-SEEDED WHEAT AND RAPESEED

1983 ◽  
Vol 63 (1) ◽  
pp. 299-301 ◽  
Author(s):  
S. FREYMAN ◽  
G. B. SCHAALJE
Keyword(s):  
Triticum Aestivum ◽  
Winter Wheat ◽  
Spring Wheat ◽  
Detrimental Effect ◽  
Brassica Campestris ◽  
Harmful Effect ◽  
Triticum Aestivum L ◽  
Causative Effect ◽  
Effect On Yield

Where winter wheat (Triticum aestivum L. ’Norstar’) was worked-down on 1 May and the plots reseeded to spring wheat immediately, no detrimental effect on yield of spring wheat was found. However, delaying this action until 15 May reduced the yields of spring-seeded wheat because of the harmful effect of decomposing winter wheat and late seeding. Moisture depletion by winter wheat was eliminated as a causative effect by light irrigations during May. Yields of rapeseed (Brassica campestris L. ’Candle’) were not so severely reduced by worked-down winter wheat. The harmful effect was significant only with 30 May cultivation and seeding date.Key words: Phytotoxicity, Triticum aestivum, Brassica campestris, worked-down

scholarly journals Impact of Soil Moisture on Crop Yields over Brazilian Semiarid

2017 ◽  
Vol 5 ◽  
Author(s):  
Luciana Rossato ◽  
Regina C. dos Santos Alvalá ◽  
José A. Marengo ◽  
Marcelo Zeri ◽  
Ana P. M. do Amaral Cunha ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Crop Yields ◽  
Brazilian Semiarid
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