Prediction of soil organic carbon for Ethiopian highlands using soil spectroscopy

Soil spectroscopy was applied for predicting soil organic carbon (SOC) in the highlands of Ethiopia. Soil samples were acquired from Ethiopia’s National Soil Testing Centre and direct field sampling. The reflectance of samples was measured using a FieldSpec 3 diffuse reflectance spectrometer. Outliers and sample relation were evaluated using principal component analysis (PCA) and models were developed through partial least square regression (PLSR). For nine watersheds sampled, 20% of the samples were set aside to test prediction and 80% were used to develop calibration models. Depending on the number of samples per watershed, cross validation or independent validation were used.The stability of models was evaluated using coefficient of determination (R2), root mean square error (RMSE), and the ratio performance deviation (RPD). The R2 (%), RMSE (%), and RPD, respectively, for validation were Anjeni (88, 0.44, 3.05), Bale (86, 0.52, 2.7), Basketo (89, 0.57, 3.0), Benishangul (91, 0.30, 3.4), Kersa (82, 0.44, 2.4), Kola tembien (75, 0.44, 1.9),Maybar (84. 0.57, 2.5),Megech (85, 0.15, 2.6), andWondoGenet (86, 0.52, 2.7) indicating that themodels were stable. Models performed better for areas with high SOC values than areas with lower SOC values. Overall, soil spectroscopy performance ranged from very good to good.

Visible near infra-red (VisNIR) spectroscopy for predicting soil organic carbon in Ethiopia

Over the past few decades, the advantages of the visible-near infra-red (VisNIR) diffuse reflectance spectrometer (DRS) method have enabled prediction of soil organic carbon (SOC). In this study, SOC was predicted using regression models for samples taken from three sites (Gununo, Maybar and Anjeni) in Ethiopia. SOC was characterized in laboratory using conventional wet chemistry and VisNIR-DRS methods. Principal component analysis (PCA), principal component regression (PCR) and partial least square regression (PLS) models were developed using Unscrambler X 10.2. PCA results show that the first two components accounted for a minimum of 96% variation which increased for individual sites and with data treatments. Correlation (r), coefficient of determination (R2) and residual prediction deviation (RPD) were used to rate four models built. PLS model (r, R2, RPD) values for Anjeni were 0.9, 0.9 and 3.6; for Gununo values 0.6, 0.3 and 1.2; for Maybar values 0.6, 0.3 and 0.9, and for the three sites values 0.7, 0.6 and 1.5, respectively. PCR model values (r, R2, RPD) for Anjeni were 0.9, 0.8 and 2.7; for Gununo values 0.5, 0.3 and 1; for Maybar values 0.5, 0.1 and 0.7, and for the three sites values 0.7, 0.5 and 1.2, respectively. Comparison and testing of models shows superior performance of PLS to PCR. Models were rated as very poor (Maybar), poor (Gununo and three sites) and excellent (Anjeni). A robust model, Anjeni, is recommended for prediction of SOC in Ethiopia.

Meta-Analysis of Long-Term Land Management Effect on Soil Organic Carbon (SOC) in Ethiopia

The role of Soil Organic Carbon (SOC) in mitigating climate change, indicating soil quality and ecosystem function has created research interested to know the nature of SOC at landscape level. The objective of this study was to examine variation and distribution of SOC in a long-term land management at a watershed and plot level. This study was based on meta-analysis of three case studies and 128 surface soil samples from Ethiopia. Three sites (Gununo, Anjeni and Maybar) were compared after considering two Land Management Categories (LMC) and three types of land uses (LUT) in quasi-experimental design. Shapiro-Wilk tests showed non-normal distribution (p = 0.002, a = 0.05) of the data. SOC median value showed the effect of long-term land management with values of 2.29 and 2.38 g kg-1 for less and better-managed watersheds, respectively. SOC values were 1.7, 2.8 and 2.6 g kg-1 for Crop (CLU), Grass (GLU) and Forest Land Use (FLU), respectively. The rank order for SOC variability was FLU>GLU>CLU. Mann-Whitney U and Kruskal-Wallis test showed a significant difference in the medians and distribution of SOC among the LUT, between soil profiles (p<0.05, confidence interval 95%, a = 0.05) while it is not significant (p>0.05) for LMC. The mean and sum rank of Mann Whitney U and Kruskal Wallis test also showed the difference at watershed and plot level. Using SOC as a predictor, cross-validated correct classification with discriminant analysis showed 46 and 49% for LUT and LMC, respectively. The study showed how to categorize landscapes using SOC with respect to land management for decision-makers.

Carbon sequestration in Himalaya's alpine meadows: Mitigating cropping encroachment on pastures in Northern Pakistan

Rangelands store about 30% of the world’s carbon and support over 120 million pastoralists globally. Adjusting the management of remote alpine pastures bears a substantial climate change mitigation potential that can provide livelihood support for marginalized pastoralists through carbon payment. Landless pastoralists in Northern Pakistan seek higher income by cropping potatoes and peas over alpine pastures. However, tilling steep slopes without terracing exposes soil to erosion. Moreover, yields decline rapidly requiring increasing fertilizer inputs. Under these conditions, carbon payment could be a feasible option to compensate pastoralists for renouncing hazardous cropping while favoring pastoral activities. The study quantifies and compares C on cropped and grazed land. The hypothesis was that cropping on alpine pastures reduces former carbon storage. The study area located in the Naran valley of the Pakistani Himalayas receives an annual average of 819 mm of rain and 764 mm of snow. Average temperatures remain below 0°C from November to March while frost may occur all year round. A total of 72 soil core samples were collected discriminating land use (cropping, pasture), aspect (North, South), elevation (low 3000, middle 3100, and high 3200 m a.s.l.), and soil depth (shallow 0-10, deep 10-30 cm). Thirty six biomass samples were collected over the same independent variables (except for soil depth) using a 10x10x20 cm steal box inserted in the ground for each sample. Aboveground biomass and coarse roots were separated from the soil aggregate and oven-dried. Soil organic carbon (SOC) and biomass carbon (BC) were estimated through a potassium dichromate oxidation treatment. The samples were collected during the second week of October 2010 at the end of the grazing and cropping season and before the first snowfall. The data was statistically analyzed by means of a one-way analysis of variance. Results show that all variables taken separately have a significant effect on mean SOC [%]: crop/pasture 1.33/1.6, North/South 1.61/1.32, low/middle/high 1.09/1.62/1.68, shallow/deep 1.4/1.53. However, for BC, only land use has a significant effect with more than twice the amount of carbon in pastures [g m-2]: crop/pasture 127/318. These preliminary findings suggest that preventing the conversion of pastures into cropping fields in the Naran valley avoids an average loss of 12.2 t C ha-1 or 44.8 t CO2eq ha-1 representing a foreseeable compensation of 672 € ha-1 for the Naran landless pastoralists who would renounce cropping. The ongoing study shall provide a complete picture for carbon payment integrating key aspects such as the rate of cropping encroachment over pastures per year, the methane leakage from the system due to livestock enteric fermentation, the expected cropping income vs. livestock income and the transaction costs of implementing the mitigation project, certifying it, and verifying carbon credits. A net present value over an infinite time horizon for the mitigation scenario shall be estimated on an iterative simulation to consider weather and price uncertainties. The study will also provide an estimate of the minimum price of carbon at which pastoralists would consider engaging in the mitigation activity.

Spatial Assessment of Erosion and its Impact on Soil Fertility in the Tajik Foothills

Efficient planning of soil conservation measures requires, first, to understand the impact of soil erosion on soil fertility with regard to local land cover classes; and second, to identify hot spots of soil erosion and bright spots of soil conservation in a spatially explicit manner. Soil organic carbon (SOC) is an important indicator of soil fertility. The aim of this study was to conduct a spatial assessment of erosion and its impact on SOC for specific land cover classes. Input data consisted of extensive ground truth, a digital elevation model and Landsat 7 imagery from two different seasons. Soil spectral reflectance readings were taken from soil samples in the laboratory and calibrated with results of SOC chemical analysis using regression tree modelling. The resulting model statistics for soil degradation assessments are promising (R2=0.71, RMSEV=0.32). Since the area includes rugged terrain and small agricultural plots, the decision tree models allowed mapping of land cover classes, soil erosion incidence and SOC content classes at an acceptable level of accuracy for preliminary studies. The various datasets were linked in the hot-bright spot matrix, which was developed to combine soil erosion incidence information and SOC content levels (for uniform land cover classes) in a scatter plot. The quarters of the plot show different stages of degradation, from well conserved land to hot spots of soil degradation. The approach helps to gain a better understanding of the impact of soil erosion on soil fertility and to identify hot and bright spots in a spatially explicit manner. The results show distinctly lower SOC content levels on large parts of the test areas, where annual crop cultivation was dominant in the 1990s and where cultivation has now been abandoned. On the other hand, there are strong indications that afforestations and fruit orchards established in the 1980s have been successful in conserving soil resources.