Basic Soil Data Requirements for Process-Based Crop Models as a Basis for Crop Diversification

An evidence base was developed to facilitate adoption of hemp ( Cannabis sativa L.) in tropical environments (Wimalasiri et al. (2021)). Agro-ecological requirements data of hemp were acquired from international databases and was contrasted against local climate and soil conditions using an augmented species ecological niche modeling. The outputs were then used to map the suitability for all locations for 12 possible calendar-year seasons within peninsular Malaysia. The most probable seasonal map was then used to generate a land suitability map for agricultural areas across 5 standard land suitability categories. Having developed the general suitability maps of hemp in Malaysia, detailed crop growth data were collected from literature and was then used to simulate an ideotype crop model (for both seed and fiber) for selected locations across Malaysia, where detailed daily climate data and soil information were available. Following the development of a downscaled future climate dataset, a simulated dataset of yield for the future conditions were also developed. Next, the simulated seed and fiber yield data were used to create yield maps for hemp across peninsular Malaysia. An economic value and cost-benefit analyses were also carried out using data that were collected from literature and local sources to simulate the true cost and benefit of growing hemp both for now and future conditions. This data provides the first ever evidence base for an underutilized crop in Southeast Asia. All data that was generated using the proposed published framework for the adoption of hemp in the future are stored in their original format in an online repository and is described in this article. The data can be used to map the suitability at finer scales, analyze and re-calibrate a yield model using any climate scenario and evaluate the economics of production using the standard methodology described in the above-mentioned publication.

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“…Soil data were collected from the Soilgrids 2.0 database ( www.soil grids.org ). The database was previously used in crop modeling studies [16] .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Datasets for the development of hemp (Cannabis sativa L.) as a crop for the future in tropical environments (Malaysia)

et al. 2022

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“…Soil sampling locations used for interpolations were previously described by Wimalasiri et al. [5] .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…For example, Wimalasiri et al. [5] has used the same dataset to create a continuous yield map of rice across the whole country. The data can be used to fill gaps in observed data which are normally scarce.…”

Section: Value Of the Datamentioning

confidence: 99%

The first version of nation-wide open 3D soil database for Sri Lanka

et al. 2020

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Soil data for Sri Lanka are available through semi-detailed series maps that were developed based on limited soil profile data combined with expert knowledge. This data plays a vital role in decisions at national and regional levels. However, the present format of this database does not allow for their wider use in crop simulation modelling and other related agricultural research that require finer scale data. This is due to the fact that cross-country profile data are not harmonised based on standard depths. Several attempts were made to produce digital soil data for Sri Lanka at different geographic scales, however, a completely harmonised data that covers variability across depths and properties is yet to be made available. In this article, we describe the first version of the open digital soil database that was developed using a database of 122 locations across the country. Soil properties were harmonised for standard depths using equal-area quadratic smoothing splines. Out of several interpolation methods that were evaluated for univariate interpolation, maps which were produced with the least overall error (RMSE) in the process of cross-validation were selected. The newly developed digital soil database contains 9 soil properties; pH, bulk density, cation exchange capacity, organic carbon, volumetric moisture content at 0.33 and 15 bars levels, sand silt and clay content. Moreover, the data are available for five standard depth layers as 0–5, 5–15, 15–30, 30–60 and 60–100 cm in raster format at 1 km spatial resolution. Both interpolated property maps and their error maps were stored in an open repository and made available for public use. The first version of all maps is also showcased online through open web mapping services. The repository will be gradually updated with higher resolution and more accurate maps as more samples become available and better interpolation method are used. This data could provide complementary information for insight generation at finer scales where limited local informaiton about soils hinders agricultural development.

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“…The relationship between climatic parameters and crop yield can be studied using different approaches such as crop modeling, linear and nonlinear regression on which they have different advantages and disadvantages. Crop models need several inputs that include weather, soil and crop parameters that hinder their applications (Wimalasiri et al, 2020). The non-linear method, which is the mostly used method in Sri Lanka consists of several drawbacks including missing the impact/ relationship of other covariates while most of the relationships are not linear.…”

Section: Statistical Analysis Of Climate Trendsmentioning

confidence: 99%

Analyzing relationships between rainfall and paddy harvest using artificial neural network (ANN) approach: case studies from North-western and North-central provinces, Sri Lanka

et al. 2022

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Purpose: Food and agriculture are frequently affected from on-going climate change. A significant percentage of annual harvest is lost due to extreme climatic conditions in different parts of the world. Sri Lanka is considered as a country which is vulnerable to climate change. Therefore, this research presents a detailed analysis to find out the non-linear relationships between the rainfall and paddy harvest in two major provinces of Sri Lanka.Research Method: North-central and North-western provinces as two major agricultural areas were selected for the study. Rainfall trends were identified using non-parametric Mann-Kendall and Sen's slope estimator tests. The artificial neural network (ANN) approach was used to establish non-linear relationships between rainfall and paddy yield.Findings: There was no significant (p > 0.05) linear correlation between rainfall amount and the rainfed paddy yield in tested locations. However, no clear relationship between the rainfall and rain fed yield were found in the 14 predefined functions (polynomial, logarithmic, exponential and trigonometric) derived using ANN where the calculated coefficients of determination were less than 0.3. Research Limitations:Due to lack of other climate variables such as temperatures, a significant relationship was not observed in this study. Originality/value:We have shown that non-linear artificial neural network approach can be used to study the impact of climate on agricultural production in Sri Lanka.

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Basic Soil Data Requirements for Process-Based Crop Models as a Basis for Crop Diversification

Cited by 24 publications

References 49 publications

Datasets for the development of hemp (Cannabis sativa L.) as a crop for the future in tropical environments (Malaysia)

Datasets for the development of hemp (Cannabis sativa L.) as a crop for the future in tropical environments (Malaysia)

The first version of nation-wide open 3D soil database for Sri Lanka

Analyzing relationships between rainfall and paddy harvest using artificial neural network (ANN) approach: case studies from North-western and North-central provinces, Sri Lanka

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