Soil Organic carbon (SOC) is vital to the soil’s ecosystem functioning as well as improving soil fertility. Slight variation in C in the soil has significant potential to be either a source of CO₂ in the atmosphere or a sink to be stored in the form of soil organic matter. However, modeling SOC spatiotemporal changes was challenging due to lack of data to represent the high spatial heterogeneity in soil properties. Less expensive techniques, digital soil mapping (DSM) combined with space-for-time substitution (SFTS), were applied to predict the present and projected SOC stock for temperature and rainfall projections under different climate scenarios represented by the four Representative Concentration Pathways (RCPs): RCP2.6, RCP4.5, RCP6, and RCP8.5). The relationship between environmental covariates (n = 16) and measured SOC stock (148 samples) was developed using a random forest model. Then, the temporal changes in SOC stock over the baseline were developed for the top 30 cm soil depth of the selected districts (Chiro Zuria, Kuni, Gemechis and Mieso) of West Hararghe Zone at 30 m resolution. The model validation using the random sample of 20% of the data showed that the model explained 44% of the variance (R²) with a root mean square error (RMSE) of 8.96, a mean error (ME) of 0.16, and a Lin’s concordance correlation coefficient (CCC) of 0.88. Temperature was the most important predictor factor influencing the spatial distribution of SOC stock. An overall net gain of SOC stock over the present C stock was expected in the study area by 2050. The gain in areas with the lower baseline SOC stock counterbalanced the loss in areas with the higher baseline stock. The changes in the SOC stock depended on land use land cover (LULC), soil type, and agro-ecological zones. By 2050, cropland is supposed to lose its SOC stock under all RCPs; therefore, appropriate decisions are crucial to compensate for the loss of C.

Nitrogen stress is one of the key factors limiting sorghum crop productivity. To identify desirable sorghum genotypes best suited to nitrogen stress, the response of 300 sorghum landraces and two standard checks: KS78, and BCK60 were evaluated using yield-based indices. The experiment was conducted under nitrogen stress and ideal conditions for two years using a split-plot in RCBD design with two replications. Nitrogen treatments were assigned to the main plots, and sorghum genotypes were assigned to the subplots. Fourteen nitrogen stress screening indices, namely, the yield stability index (YSI), stress tolerance index (STI), modified stress tolerance index (KSTI), low nitrogen tolerance index (LNTI), relative decrease in yield (RDY), tolerance index (TOL), yield index (YI), stress susceptibility index (SSI), stress susceptibility percentage index (SSPI), and harmonic mean (HM), were computed using grain yield. The yield under optimal and stress conditions was significantly positively correlated with the HM, MP, GMP, STI, HM, YI, and modified STI. The results from the cluster analysis, PCA biplot, and 3D biplot classified the genotypes into three groups: nitrogen stress tolerant, nitrogen stress susceptible, and poor performers under both environments. Using STI, GMP, and MP, Genotypes (G80) ETSL101255, (G216) ETSL101006, (G35) ETSL101258, (G146) ETSL100813, and genotypes (G244) ETSL100292, (G161) ETSL100539, (G240) ETSL100890, (G93) ETSL101845, (G261) ETSL100625, (G248) ETSL100874, (G258) ETSL100752, (G142) ETSL100921, (G135) ETSL100307 were identified as top nitrogen stress tolerant, and susceptible genotypes respectively. We conclude that the indices MP, GMP, and STI can help in improving sorghum yield by selecting nitrogen efficient genotypes.