A Deep Learning Approach for Multi-Depth Soil Water Content Prediction in Summer Maize Growth Period

The transformation process of soil water plays an important role in the hydrological cycle, and is a link to other water processes. Study on the regularity of soil water transformation under agricultural plantation is favorable to understanding the influence of human activities on soil water conversion. Typical crop was selected in Beijing-Tianjin-Hebei(BTH) region and the study on regularity of field-scale soil water transformation was carried out by means of crop-soil water field experimental observation combined with model simulation. In the field experiment, testing and observation of irrigated and rainfed maize were simultaneously carried out in the adjacent fields respectively to form a comparative experimental study. The experimental observation data were used to establish the soil water model, which is calibrated in many aspects, such as field water content change during the maize growth period, the soil profile water content distribution at different moments, maize leaf area index and plant height. The results show that this model has an efficient simulation effect. Quantitative study on field evapotranspiration regularity, field soil water flux under irrigated and rainfed modes, impact mechanism of soil water deep seepage during maize growth period was achieved through the simulation of soil water process, and related reference conclusions were also proposed for water resources management and conservation in BTH.

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Tillage practices affects the grain filling of inferior kernel of summer maize by regulating soil water content and photosynthetic capacity

Agricultural Water Management ◽

10.1016/j.agwat.2020.106600 ◽

2021 ◽

Vol 245 ◽

pp. 106600

Author(s):

Lichao Zhai ◽

Zhanbiao Wang ◽

Shijia Song ◽

Lihua Zhang ◽

Zhengbin Zhang ◽

...

Keyword(s):

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Photosynthetic Capacity ◽

Grain Filling ◽

Summer Maize ◽

Tillage Practices

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Relationship of leaf water content with photosynthesis and soil water content in summer maize

Acta Ecologica Sinica ◽

10.5846/stxb201612092534 ◽

2018 ◽

Vol 38 (1) ◽

Author(s):

冯晓钰 FENG Xiaoyu ◽

周广胜 ZHOU Guangsheng

Keyword(s):

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Leaf Water ◽

Leaf Water Content ◽

Summer Maize ◽

Relationship Of

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Differential Influence of No-Tillage and Precipitation Pulses on Soil Heterotrophic and Autotrophic Respiration of Summer Maize in the North China Plain

Agronomy ◽

10.3390/agronomy10122004 ◽

2020 ◽

Vol 10 (12) ◽

pp. 2004

Author(s):

Kun Du ◽

Fadong Li ◽

Peifang Leng ◽

Zhao Li ◽

Chao Tian ◽

...

Keyword(s):

Soil Temperature ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

North China Plain ◽

North China ◽

Summer Maize ◽

Tillage Practices ◽

The North ◽

Precipitation Pulses

It is important to strengthen the studies on the response of soil respiration components to tillage practices and natural precipitation in cropland. Therefore, soil heterotrophic respiration (RH) and autotrophic (RA) respiration were monitored by a root exclusion method in the North China Plain (NCP). The tillage practices included no-tillage (NT) and conventional tillage (CT), and the study periods were the summer maize growth stages in 2018 and 2019. RH, RA, soil water content and temperature were measured continuously for 113 days by an automatic sampling and analysis system. The soil RH values on bright days and rain-affected days were higher under NT in 2018 (14.22 and 15.06 g CO2 m−2 d−1, respectively) than in 2019 (8.25 and 13.30 g CO2 m−2 d−1, respectively). However, the RA values on bright days and rain-affected days were lower under NT in 2018 (4.74 and 4.97 g CO2 m−2 d−1, respectively) than in 2019 (5.67 and 6.93 g CO2 m−2 d−1, respectively). Moreover, NT decreased RH but increased RA compared to CT in 2019. Compared to bright days, the largest increase in both RH and RA after rain pulses was under CT in 2019 (6.75 and 1.80 g CO2 m−2 d−1, respectively). Soil water content and soil temperature were higher in 2018 than in 2019. Moreover, NT increased soil water content and decreased soil temperature on bright days compared to CT in 2019. Furthermore, soil temperature accounted for more variations in RH on bright days and rain-affected days, but soil water content had a greater influence on RA on bright days. However, after precipitation, higher soil water content decreased RA under NT in 2018, while soil water content was positively related to RA under CT in 2019. This study determined the differential response of RH and RA to tillage practices and natural precipitation pulses, and we confirmed that excessively dry soil increases soil carbon loss after rain events in the NCP.

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Modeling Soil Water Content and Reference Evapotranspiration from Climate Data Using Deep Learning Method

Applied Sciences ◽

10.3390/app11115029 ◽

2021 ◽

Vol 11 (11) ◽

pp. 5029

Author(s):

Khadijeh Alibabaei ◽

Pedro D. Gaspar ◽

Tânia M. Lima

Keyword(s):

Deep Learning ◽

Decision Support ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Mean Square Error ◽

Loss Function ◽

Reference Evapotranspiration ◽

Learning Algorithms ◽

Mean Square

In recent years, deep learning algorithms have been successfully applied in the development of decision support systems in various aspects of agriculture, such as yield estimation, crop diseases, weed detection, etc. Agriculture is the largest consumer of freshwater. Due to challenges such as lack of natural resources and climate change, an efficient decision support system for irrigation is crucial. Evapotranspiration and soil water content are the most critical factors in irrigation scheduling. In this paper, the ability of Long Short-Term Memory (LSTM) and Bidirectional LSTM (BLSTM) to model daily reference evapotranspiration and soil water content is investigated. The application of these techniques to predict these parameters was tested for three sites in Portugal. A single-layer BLSTM with 512 nodes was selected. Bayesian optimization was used to determine the hyperparameters, such as learning rate, decay, batch size, and dropout size.The model achieved the values of mean square error values within the range of 0.014 to 0.056 and R2 ranging from 0.96 to 0.98. A Convolutional Neural Network (CNN) model was added to the LSTM to investigate potential performance improvement. Performance dropped in all datasets due to the complexity of the model. The performance of the models was also compared with CNN, traditional machine learning algorithms Support Vector Regression, and Random Forest. LSTM achieved the best performance. Finally, the impact of the loss function on the performance of the proposed models was investigated. The model with the mean square error as loss function performed better than the model with other loss functions.

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SOIL WATER CONTENT AFFECTS THE AVAILABILITY OF PHOSPHATE

Proceedings of the New Zealand Grassland Association ◽

10.33584/jnzg.1985.46.1696 ◽

1985 ◽

pp. 185-189

Author(s):

M.C.H.Mouat Pieter Nes

Keyword(s):

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Soil Solution ◽

Equivalent Reduction

Reduction in water content of a soil increased the concentration of ammonium and nitrate in solution, but had no effect on the concentration of phosphate. The corresponding reduction in the quantity of phosphate in solution caused an equivalent reduction in the response of ryegrass to applied phosphate. Keywords: soil solution, soil water content, phosphate, ryegrass, nutrition.

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Effect of inter-row cultivation on soil carbon dioxide emission in a peach plantation

Agrokémia és Talajtan ◽

10.1556/agrokem.59.2010.1.19 ◽

2010 ◽

Vol 59 (1) ◽

pp. 157-164 ◽

Cited By ~ 1

Author(s):

E. Tóth ◽

Cs. Farkas

Keyword(s):

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Carbon Dioxide Emission ◽

Nutrient Content ◽

Soil Disturbance ◽

Biological Properties ◽

Soil Tillage ◽

Favourable Effect ◽

Volumetric Soil Water Content

Soil biological properties and CO2emission were compared in undisturbed grass and regularly disked rows of a peach plantation. Higher nutrient content and biological activity were found in the undisturbed, grass-covered rows. Significantly higher CO2fluxes were measured in this treatment at almost all the measurement times, in all the soil water content ranges, except the one in which the volumetric soil water content was higher than 45%. The obtained results indicated that in addition to the favourable effect of soil tillage on soil aeration, regular soil disturbance reduces soil microbial activity and soil CO2emission.

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