In-situ soil moisture conservation: utilisation and management of rainwater for crop production

2014 ◽  
Vol 10 (3) ◽  
pp. 295
Author(s):  
Peter Kathuli ◽  
J.K. Itabari
Keyword(s):  
Soil Moisture ◽  
Crop Production ◽  
Soil Moisture Conservation

scholarly journals Evaluating the Effects of In-situ Rainwater Harvesting Techniques on Soil Moisture Conservation and Grain Yield of Maize (Zea mays L.) in Fedis District, Eastern Hararghe, Ethiopia

2018 ◽  
Vol 6 (9) ◽  
pp. 1129 ◽  
Author(s):  
Amisalu Milkias ◽  
Teshale Tadesse ◽  
Habtamu Zeleke
Keyword(s):  
Soil Moisture ◽  
Grain Yield ◽  
Crop Production ◽  
Rainwater Harvesting ◽  
Growth Stages ◽  
Soil Moisture Storage ◽  
Soil Moisture Conservation ◽  
Moisture Storage ◽  
Harvesting Techniques

In the drier farming regions of the world, where crop production is constrained by short growing period, unpredictable and short rainfall with sporadic run-off, in-situ rainwater harvesting is vital for successful crop production. In connection to this, a study was conducted in Fedis district of Oromia region during the main rainy seasons of 2015 and 2016 to evaluate the effects of in-situ rainwater harvesting techniques (Ridge Furrow (RF), Contour Ridge (CR), and Tied Ridge (TR)) on soil moisture conservation and grain yield of maize. A spilt-plot design was used and soil moisture content was measured at three growth stages of the crop to a depth of 60 cm with 20 cm interval. The results showed that water harvesting techniques significantly increased moisture conservation compared to the control, which was flat bed preparation. Averaged over the three stages, the TR, CR and RF treatments increased soil moisture storage by 134.59, 128.57, and 121.87%, respectively, compared to the control. The study also revealed that the in-situ rainwater harvesting techniques, due to the improved soil moisture storage, significantly affected grain yield of the maize. Averaged over the two years, the TR, CR, and FR increased the grain yield 143.14, 131.47 and 121.16%, respectively, over the control treatment. Therefore, in drier environments, such as Fedis, in-situ rainwater harvesting techniques can be recommended for better moisture conservation and subsequent improvement in crop production.

Improving Dryland Crop Simulation by Assimilating Soil Moisture and Vegetation Data

2020 ◽  
Author(s):  
Yang Lu ◽  
Justin Sheffield
Keyword(s):  
Soil Moisture ◽  
Crop Production ◽  
Management Practices ◽  
Water Productivity ◽  
Vegetation Indices ◽  
Screening Method ◽  
Model Performance ◽  
State Parameter ◽  
Aquacrop Model

<p>Global population is projected to keep increasing rapidly in the next 3 decades, particularly in dryland regions of the developing world, making it a global imperative to enhance crop production. However, improving current crop production in these regions is hampered by yield gaps due to poor soils, lack of irrigation and other management practices. Here we develop a crop modelling capability to help understand gaps, and apply to dryland regions where data for parametrizing and testing models is generally lacking. We present a data assimilation framework to improve simulation capability by assimilating in-situ soil moisture and vegetation data into the FAO AquaCrop model. AquaCrop is a water-driven model that simulates canopy growth, biomass and crop yield as a function of water productivity. The key strength of AquaCrop lies in the low requirement for input data thanks to its simple structure. A global sensitivity analysis is first performed using the Morris screening method and the variance-based Extended Fourier Amplitude Sensitivity Test (EFAST) method to identify the key influential parameters on the model outputs. We begin with state-only updates by assimilating different combinations of soil moisture and vegetation data (vegetation indices, biomass, etc.), and different filtering/smoothing assimilation strategies are tested. Based on the state-only assimilation results, we further evaluate the utility of joint state-parameter (augmented-states) assimilation in improving the model performance. The framework will eventually be extended to assimilate remote sensing estimates of soil moisture and vegetation data to overcome the lack of in-situ data more generally in dryland regions.</p>

scholarly journals Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations

2021 ◽  
Author(s):  
Shannon de Roos ◽  
Gabriëlle J. M. De Lannoy ◽  
Dirk Raes
Keyword(s):  
Soil Moisture ◽  
Biomass Production ◽  
Crop Production ◽  
Agricultural Land ◽  
Surface Soil ◽  
Processing System ◽  
Surface Soil Moisture ◽  
Aquacrop Model ◽  
Global Land

Abstract. The current intensive use of agricultural land is affecting the land quality and contributes to climate change. Feeding the world’s growing population under changing climatic conditions demands a global transition to more sustainable agricultural systems. This requires good insight in land cultivation practices at the field to global scale. This study outlines a spatially distributed version of the field-scale crop model AquaCrop version 6.1, to simulate agricultural biomass production and soil moisture variability over Europe at a relatively fine resolution of 30 arcseconds (~1 km). A highly efficient parallel processing system is implemented to run the model regionally with global meteorological input data from the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), soil textural information from the Harmonized World Soil Database, version 1.2 (HWSDv1.2), and generic crop information. Daily crop biomass production is evaluated with the Copernicus Global Land Service dry matter productivity (CGLS-DMP) data. Surface soil moisture is compared against NASA Soil Moisture Active Passive surface soil moisture (SMAP-SSM) retrievals, the Copernicus Global Land Service surface soil moisture (CGLS-SSM) product derived from Sentinel-1, and in situ data from the International Soil Moisture Network (ISMN). Over central Europe, the regional AquaCrop model is able to capture the temporal variability in both biomass production and soil moisture, with a spatial mean correlation of 0.8 (CGLS-DMP), 0.74 (SMAP-SSM) and 0.52 (CGLS-SSM), respectively. The higher performance when evaluating with SMAP-SSM compared to Sentinel-1 CGLS-SSM is largely due to the lower quality of CGLS-SSM satellite retrievals under growing vegetation. The regional model further captures the interannual variability, with a mean anomaly correlation of 0.46 for daily biomass, and mean anomaly correlations of 0.65 (SMAP-SSM) and 0.50 (CGLS-SSM) for soil moisture. It is shown that soil textural characteristics and irrigated areas influence the model performance. Overall, the regional AquaCrop model proves to be useful in assessing crop production and soil moisture at various scales and could serve as a bridge between point-based and global models.

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