Modeling soil organic carbon in corn (
            <i>Zea mays</i>
            L.)-based systems in Ohio under climate change

Maas, Ellen D.v.L.; Lal, Rattan; Coleman, Kevin; Montenegro, A.; Dick, Warren A.

doi:10.2489/jswc.72.3.191

Cited by 8 publications

(4 citation statements)

References 60 publications

(54 reference statements)

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“…In addition, majority of farmers use only chemical fertilizers without any organic supplement. Meanwhile, organic fertilizers are released slowly, and the results are not visible (Maas et al, 2017). The application of chemical fertilizers has a direct effect on plants (Asvini & Jithesh, 2018), therefore, most farmers do not use organic fertilizers.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

“…A previous study stated that fertilizer and pesticide residues settle in the soil thereby increasing its acidity (Asvini & Jithesh, 2018). Other impacts observed include lower availability of nutrients, organic matter, and cation exchange capacity (Hung et al, 2019;Maas et al, 2017). Agricultural extensification is usually conducted by land conversion and forest clearing.…”

Section: Introductionmentioning

confidence: 99%

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Mapping of Soil Quality Index (SQI) for Paddy Fields Using Sentinel-2 Imagery, Laboratory Analysis, and Principal Component Analysis

et al. 2021

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The use of intensive chemical inputs causes lower availability of nutrients, organic matter, cation exchange capacity, and soil degradation.Therefore, this study aims to assess the soil quality index (SQI) for paddy fields in Jember, East Java, Indonesia. Input data for this study consist of land cover (interpreted from the Sentinel-2 image), soil type, and slope maps. Furthermore, the procedure to calculate soil quality index (SQI) include (1) spatial analysis to create the land unit, (2) preparation of soil sampling, (3) soil chemical analysis, (4) principal component analysis (PCA), and (5) reclassifying soil quality index (SQI). The PCA results showed that three variables i.e., % sand, total- P, and % silt were strongly correlated to SQI, while three classes namely very low, low, and medium of SQI were sufficiently used to describe the spatial variability of the paddy field. Furthermore, approximately 41.14% of the paddy field area were classed as very low while 52.23%, and 6.63% were categorized as low and medium SQI respectively. Based on the results, about 93.37% of paddy fields in Jember Regency still require improvement in soil quality via the addition of ameliorants such as organic fertilizers to increase quality and productivity. This application needs to focus on areas with very low-low quality hence, the quality increased to the medium category. Keywords : Mapping; Soil Quality Index (SQI); PCA; Paddy field Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

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Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping of Soil Quality Index (SQI) for Paddy Fields Using Sentinel-2 Imagery, Laboratory Analysis, and Principal Component Analysis

et al. 2021

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“…Soil organic C models are constructed using soil data from two long-term agricultural research sites in Ohio under no-till (NT) and plow-till (PT) with projected future climate change in the paper by Maas et al (2017). Results vary by site and management; however, total SOC is projected to decrease at all sites in the topsoil layers under all management and climate projections.…”

Section: Managing Corn-based Cropping Systems the Central Theme Of Pamentioning

confidence: 99%

Crops, climate, culture, and change

Morton¹,

Abendroth²

2017

Journal of Soil and Water Conservation

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“…Since NT management can increase soil water infiltration rate and water‐holding capacity, NT is often considered an important means of adapting to the altered rainfall patterns associated with climate change (Powlson et al, 2014). However, the impact of climate change on the SOC stocks in NT that contribute to this adaptation potential is poorly understood (Pan et al, 2010; van Groenigen et al, 2014; Maas et al, 2017). Mechanistic process‐based models of SOC dynamics can be used to evaluate these changes (Paustian et al, 2016).…”

mentioning

confidence: 99%

Simulated Soil Organic Carbon Changes in Maryland Are Affected by Tillage, Climate Change, and Crop Yield

et al. 2018

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The impact of climate change on soil organic C (SOC) stocks in no-till (NT) and conventionally tilled (CT) agricultural systems is poorly understood. The objective of this study was to simulate the impact of projected climate change on SOC to 50-cm soil depth for grain cropping systems in the southern Mid-Atlantic region of the United States. We used SOC and other data from the long-term Farming Systems Project in Beltsville, MD, and CQESTR, a process-based soil C model, to predict the impact of cropping systems and climate (air temperature and precipitation) on SOC for a 40-yr period . Since future crop yields are uncertain, we simulated five scenarios with differing yield levels (crop yields from 1996-2014, and at 10 or 30% greater or lesser than these yields). Without change in climate or crop yields (baseline conditions) CQESTR predicted an increase in SOC of 0.014 and 0.021 Mg ha −1 yr −1 in CT and NT, respectively. Predicted climate change alone resulted in an SOC increase of only 0.002 Mg ha −1 yr −1 in NT and a decrease of 0.017 Mg ha −1 yr −1 in CT. Crop yield declines of 10 and 30% led to SOC decreases between 2 and 8% compared with 2012 levels. Increasing crop yield by 10 and 30% was sufficient to raise SOC 2 and 7%, respectively, above the climate-only scenario under both CT and NT between 2012 and 2052. Results indicate that under these simulated conditions, the negative impact of climate change on SOC levels could be mitigated by crop yield increases.

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Modeling soil organic carbon in corn ( Zea mays L.)-based systems in Ohio under climate change

Cited by 8 publications

References 60 publications

Mapping of Soil Quality Index (SQI) for Paddy Fields Using Sentinel-2 Imagery, Laboratory Analysis, and Principal Component Analysis

Mapping of Soil Quality Index (SQI) for Paddy Fields Using Sentinel-2 Imagery, Laboratory Analysis, and Principal Component Analysis

Crops, climate, culture, and change

Simulated Soil Organic Carbon Changes in Maryland Are Affected by Tillage, Climate Change, and Crop Yield

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