Modelling cropland expansion and its drivers in Trans Nzoia County, Kenya

Kipkulei, Harison Kiplagat; Bellingrath-Kimura, Sonoko Dorothea; Lana, Marcos; Ghazaryan, Gohar; Boitt, Mark; Sieber, Stefan

doi:10.1007/s40808-022-01475-7

Cited by 12 publications

(3 citation statements)

References 67 publications

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“…The prominent cause of forest loss is the expansion of development projects, including settlements and cropland, into forested area [6]. In east African, cropland expansion has led to losses in forest, wetland and grassland LULC types [83]. Hossain et al [6] has reported similar results in Bangladesh where Builtup (settlement) expanded due to massive human migration into urban areas.…”

Section: Multi-temporal Changes In Lulc From 2000 To 2020mentioning

confidence: 88%

Multi-decade land cover/land use dynamics and future predictions for Zambia: 2000–2030

Chisanga,

Phiri,

Mubanga

2024

Discov Environ

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Human LULCC is the many driver of environmental changes. Accurate and up-to-date current and predicted information on LULCC is important in land use planning and natural resource management; however, in Zambia, detailed information on LULCC is insufficient. Therefore, this study assessed the dynamics of LULC change (2000–2020) and future projections (2020–2030) for Zambia. The ESA CCI land cover maps, which have been developed from Sentinel-2 images were used in this study. This dataset has a grid spatial resolution of 300 m for the 2000, 2010 and 2020. The 31 ESA CCI Classification were reclassified into ten (10) local Classifications using the r.class module in QGIS 2.18.14. The 2000 and 2010 LULC maps were used to simulate the 2020 LULC scenario using Artificial Neural Network (Multi-layer Perception) algorithms in Modules for Land Use Change Evaluation (MOLUSCE) plugin in QGIS 2.18.14. The 2010 and 2020 maps were used to predict the 2030 LULC classes. The reference 2020 and predicted 2020 LULC maps were used to validate the model. Predicted against observed 2020 LULC map, Kappa (loc) statistic was 0.9869. The 2020 LULC patterns was successfully simulated using ANN-MLP with accuracy level of 95%. LULC classes were predicted for 2030 using the 2010–2020 calibration period. The predicted 2030 LULC types shows an increase in built-up (71.44%) and decrease in cropland (0.73%) with reference to 2020 LULC map. Dense forest (0.19%), grassland (0.85%) and bare land (1.37%) will reduce from 2020–2030. However, seasonally flooded, sparse forest, shrub land, wetland and water body will increase marginally. The largest LULC change is from forest into other LULC types. The insights from this study show that ANN-MLP can be used to predict LULCC, and that the generated information can be employed in land use planning and National Adaptation Plans at regional and national scale.

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Section: Multi-temporal Changes In Lulc From 2000 To 2020mentioning

confidence: 88%

Multi-decade land cover/land use dynamics and future predictions for Zambia: 2000–2030

Chisanga,

Phiri,

Mubanga

2024

Discov Environ

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“…First, a cropland mask for the study area was generated by combining land cover products from the FAO, the crop mapping initiative for GEOGLAM (Group on Earth Observations Global Agricultural Monitoring Initiative) Kenya country‐level support, and a prior land use and land cover change analysis conducted by Kipkulei, Bellingrath‐Kimura, Lana, Ghazaryan, Boitt et al. (2022). The second step involved identifying the dominant crops through crop type classification analysis.…”

Section: Methodsmentioning

confidence: 99%

Agronomic management response in maize (Zea maysL.) production across three agroecological zones of Kenya

Kipkulei,

Bellingrath‐Kimura,

Lana

et al. 2024

Agrosystems Geosci & Env

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Maize (Zea mays L.) productivity in Kenya has witnessed a decline attributed to the effects of climate change and biophysical constraints. The assessment of agronomic practices across agroecological zones (AEZs) is limited by inadequate data quality, hindering a precise evaluation of maize yield on a large scale. In this study, we employed the DSSAT‐CERES‐Maize crop model (where CERES is Crop Environment Resource Synthesis and DSSAT is Decision Support System for Agrotechnology Transfer) to investigate the impacts of different agronomic practices on maize yield across different AEZs in two counties of Kenya. The model was calibrated and evaluated with observed grain yield, biomass, leaf area index, phenology, and soil water content from 2‐year experiments. Remote sensing (RS) images derived from the Sentinel‐2 satellite were integrated to delineate maize areas, and the resulting information was merged with DSSAT‐CERES‐Maize yield simulations. This facilitated a comprehensive quantification of various agronomic measures at pixel scales. Evaluation of agronomic measures revealed that sowing dates and cultivar types significantly influenced maize yield across the AEZs. Notably, AEZ II and AEZ III exhibited elevated yields when implementing combined practices of early sowing and cultivar H614. The impacts of optimal management practices varied across the AEZs, resulting in yield increases of 81, 115, and 202 kg ha−1 in AEZ I, AEZ II, and AEZ III, respectively. This study underscores the potential of the CERES‐Maize model and high‐resolution RS data in estimating production at larger scales. Furthermore, this integrated approach holds promise for supporting agricultural decision‐making and designing optimal strategies to enhance productivity while accounting for site‐specific conditions.

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“…The prominent cause of forest loss is the expansion of development projects, including settlements and cropland, into forested area(Hossain et al 2023). In east African, cropland expansion has led to losses in forest, wetland and grassland LULC types(Kipkulei et al 2022). Hossain et al (2023) has reported similar results in Bangladesh where Built-up (settlement) expanded due to massive human migration into urban areas.…”

mentioning

confidence: 99%

Multi-Decade Land Cover/Land Use Dynamics and Future Predictions for Zambia: 2000 - 2030

Chisanga,

Phiri,

Mubanga

2023

Preprint

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Accurate and up-to-date information on land use/land cover change (LULCC) is important in land use planning and natural resource management; however, in sub-Saharan Africa, detailed information on LULCC is still lacking. Therefore, this study assessed the dynamics of LULC change (2000–2020) and future projections (2020–2030) for Zambia. The 2000 and 2010 LULC maps were used to simulate the 2020 LULC scenario using Artificial Neural Network (Multi-layer Perception) algorithms in Modules for Land Use Change Evaluation (MOLUSCE) plugin in QGIS 2.18.14. The 2010 and 2020 maps were used to predict the 2030 LULC classes. The reference 2020 and predicted 2020 LULC maps were used to validate the model. The validation between the predicted and observed 2020 LULC map, Kappa (loc) was 0.9869. The ANN-MLP simulated the 2020 LULC patterns successfully as indicated by the high accuracy level of more than 95%. LULC classes were predicted for 2030 using the 2010–2020 calibration period. The expected LULC types for 2030 revealed that built-up area will increase by 447.20 km2 (71.44%), while 327.80 km2 (0.73%) of cropland will be lost relative to 2020 LULC map. Dense forest (0.19%), grassland (0.85%) and bare land (1.37%) will reduce from 2020–2030. However, seasonally flooded, sparse forest, shrub land, wetland and water body will increase marginally. The largest LULC change is from forest into other LULC types. The insights from this study show that ANN-MLP can be used to predict LULCC, and that the generated information can be employed in land use planning at a national scale.

show abstract

Modelling cropland expansion and its drivers in Trans Nzoia County, Kenya

Cited by 12 publications

References 67 publications

Multi-decade land cover/land use dynamics and future predictions for Zambia: 2000–2030

Multi-decade land cover/land use dynamics and future predictions for Zambia: 2000–2030

Agronomic management response in maize (Zea maysL.) production across three agroecological zones of Kenya

Multi-Decade Land Cover/Land Use Dynamics and Future Predictions for Zambia: 2000 - 2030

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