Understanding traditional agro-ecosystem dynamics in response to systematic transition processes and rainfall variability patterns at watershed-scale in Southern Ethiopia

Gessesse, Berhan; Tesfamariam, Birhane Gebrehiwot; Melgani, Farid

doi:10.1016/j.agee.2021.107832

Cited by 4 publications

(4 citation statements)

References 26 publications

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“…Uncertainty sources in comparison to studies conducted in other countries on beef ca le greenhouse gas emissions primarily stem from the lack of activity level data and variability in livestock feed absorption. In the simulated scenario of this study, the fluctuation ranges of CH4 and N emissions correspond to previous research [35]. The CH4 fluctuation range was between 86% and 98%, and the N emission fluctuation range was between 57% and 82% [36].…”

Section: Discussionsupporting

confidence: 74%

“…In the emission estimation process, there was a lack of relevant data on feces management processes in Jilin Province, such as the parameters MS(T,S), EF3(S), and EF4, which resulted in errors in N emission data [31]. But, this result is consistent with the data in the relevant references [33][34][35]. The accuracy of N emission data can be improved by collecting more accurate data on feces management processes in future research.…”

Section: Discussionmentioning

confidence: 69%

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Simulation and Prediction of Greenhouse Gas Emissions from Beef Cattle

et al. 2023

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Greenhouse gas emission is a key issue in the sustainable development of agriculture. To effectively predict the greenhouse gas emissions of beef cattle, a model is proposed based on system dynamics and greenhouse gas emission calculation methods, and a scenario is set as a ‘Straw to Beef’ project in Jilin Province. The model was built on a baseline emission scenario (feed precision: 60%, breeding environment: dry fattening farm, corn straw utilization: burning straw), with single- and comprehensive emission reduction scenarios considered, predicting trends and reduction potentials in greenhouse gas emissions from cattle breeding and straw burning in Jilin Province from 2013 to 2028, measured in CO2-eq (CO2 equivalent). The model also explored the impact of 11 controllable variables on greenhouse gas emissions. Results showed that (1) From 2013 to 2022, greenhouse gas emissions from straw burning and cattle breeding in Jilin Province increased significantly and had an annual growth rate of 6.51% in 2020. (2) Single emission reduction scenarios showed an increasing trend in greenhouse gas emissions, while comprehensive emission reduction scenarios showed a decreasing trend. Among them, the S2.2.1 scenario (feed precision: 80%, breeding environment: livestock barn manure pit, corn straw utilization: burning straw) had the strongest emission reduction ability in the single reduction scenario, the S3.2.2 scenario (feed precision: 80%, breeding environment: livestock barn manure pit, corn straw utilization: Feed-processing straw) had the strongest emission reduction ability in the comprehensive reduction scenario, reducing emissions by 5.10% and 69.24%, respectively, compared to the baseline scenario. This suggests that the comprehensive emission reduction scenarios which utilized straw resources reasonably can greatly reduce agricultural greenhouse gas emissions. (3) The optimal emission reduction scenario indicated that the higher the proportion of digestible energy in beef cattle’s total energy intake, the more perfect the fecal treatment process, and the higher the utilization rate of straw feed, the lower the greenhouse gas emissions. Therefore, to effectively reduce greenhouse gas emissions from cattle breeding and straw burning in Jilin Province, it is important to implement comprehensive emission reduction scenarios prioritizing the efficient utilization of straw resources and improving beef cattle management practices.

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Section: Discussionsupporting

confidence: 74%

Section: Discussionmentioning

confidence: 69%

Simulation and Prediction of Greenhouse Gas Emissions from Beef Cattle

et al. 2023

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“…In the field of cropland area estimation, some research has been conducted for different regions within Ethiopia, mostly based on optical imagery. In most studies, Landsat data have been used [47][48][49], sometimes in combination with other remote sensing imagery such as IKONOS, Worldview, SPOT PROBA-V, and SRTM DEM data [50][51][52]. Other studies applied imagery from WorldView [53] and black and white photography [54].…”

Section: Introductionmentioning

confidence: 99%

“…Other studies applied imagery from WorldView [53] and black and white photography [54]. Methods applied included logistic regression [50], supervised maximum likelihood classification [49], generalized additive models [52], object-based analyses [53], and random-forest algorithms [54].…”

Section: Introductionmentioning

confidence: 99%

Cropland and Crop Type Classification with Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine for Agricultural Monitoring in Ethiopia

Eisfelder,

Boemke,

Gessner

et al. 2024

Remote Sensing

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Cropland monitoring is important for ensuring food security in the context of global climate change and population growth. Freely available satellite data allow for the monitoring of large areas, while cloud-processing platforms enable a wide user community to apply remote sensing techniques. Remote sensing-based estimates of cropped area and crop types can thus assist sustainable land management in developing countries such as Ethiopia. In this study, we developed a method for cropland and crop type classification based on Sentinel-1 and Sentinel-2 time-series data using Google Earth Engine. Field data on 18 different crop types from three study areas in Ethiopia were available as reference for the years 2021 and 2022. First, a land use/land cover classification was performed to identify cropland areas. We then evaluated different input parameters derived from Sentinel-2 and Sentinel-1, and combinations thereof, for crop type classification. We assessed the accuracy and robustness of 33 supervised random forest models for classifying crop types for three study areas and two years. Our results showed that classification accuracies were highest when Sentinel-2 spectral bands were included. The addition of Sentinel-1 parameters only slightly improved the accuracy compared to Sentinel-2 parameters alone. The variant including S2 bands, EVI2, and NDRe2 from Sentinel-2 and VV, VH, and Diff from Sentinel-1 was finally applied for crop type classification. Investigation results of class-specific accuracies reinforced the importance of sufficient reference sample availability. The developed methods and classification results can assist regional experts in Ethiopia to support agricultural monitoring and land management.

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Identification of ecological improvement zones in different ecological functional zones in northwest Hubei, China

Shao,

Yang

2023

Ecological Indicators

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Understanding traditional agro-ecosystem dynamics in response to systematic transition processes and rainfall variability patterns at watershed-scale in Southern Ethiopia

Cited by 4 publications

References 26 publications

Simulation and Prediction of Greenhouse Gas Emissions from Beef Cattle

Simulation and Prediction of Greenhouse Gas Emissions from Beef Cattle

Cropland and Crop Type Classification with Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine for Agricultural Monitoring in Ethiopia

Identification of ecological improvement zones in different ecological functional zones in northwest Hubei, China

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