Understanding rainfall variability is important to establish crop calendar related agronomic decisions. To this end, we defined start and end of seasons, analyzed dry spell and evaluated conditional risks of alternative planting dates using a thirty years daily rainfall data across southern central rift valley of Ethiopia. Results showed that the probability of annual rainfall being greater than 1000 mm was 97, 24, 94, and 61%, in Dilla, Bilate, Shamana, and Hawassa clusters, respectively. The variability of annual total rainfall in the lowland areas of Dilla and Bilate was above 25%, whereas for Shamana and Hawassa was below 20%. Variability of seasonal rainfall during FMAM was 33.7%, which is higher than ONDJ (27.1%) and JJAS (27.9%), which could lead to maize plants suffering moisture stress during FMAM season. The onset of rains had variability of 29.2, 19.5, 17.5 and 26.5%, and also LGP showed variability of 22.8, 22.1, 21.2 and 20.3% in Shamana, Bilate, Hawassa, and Dilla clusters, respectively. Moreover Shamana, Bilate, Hawassa and Dilla clusters are hit by agricultural drought in one out of 2.61, 2.3, 2.5 and 2.5 years, respectively. Model based analysis of conditional risk of farmers planting dates also showed a success rate of less than 10, 7, 40 and 63% for maize variety in Shamana, Bilate, Hawassa and Dilla clusters, respectively. However, the success rate of risk taker farmers’ is higher than anticipated by the model. The farmers who take risk were encouraged in Shamana cluster by local edaphic, physiographic, socioeconomic and climatic differences. Hence, there is a need to seek real time local agro-metrological advisory and follow the necessary tactical and strategic farming decisions. Moreover, there is also a need to incorporate local factors with modern climate models to obtain synchronized calendar estimates.

In this study, we assessed responses of adaptation options to possible climate change scenarios on maize growth and yield by using projections of 20 coupled ensemble climate models under two representative concentration pathways (RCPs) 4.5 and 8.5 by means of a DSSAT model. Growth and yield simulations were made across present and future climate conditions using the hybrid maize variety (Shone). Subsequently, simulated yields were compared with farmer’ average and on-farm trial yields. Results showed that on-farm trial yield (5.1–7.3 t ha⁻¹) lay in between farmers’ average yield (2.9–5 t ha⁻¹) and water-limited potential yield (6.3–10.6 t ha⁻¹). Maize yields achieved in farmers’ fields are projected to decline towards mid-century and further towards the end of the century regardless of the adaptation options compared with baseline in low potential clusters. Results of a combination of adaptation options including February planting, use of 64 kg ha⁻¹ N and conservation tillage provided yield advantage of 5.8% over the 30 cm till under medium GHGs emission scenario during mid-century period at Shamana. Mulching with 5 t ha⁻¹ was projected to produce a 4–5% yield advantage in the Hawassa cluster during the mid-century period regardless of changes in tillage or planting window. Under a high GHGs emission scenario, over 13.4% yield advantage was projected in the Bilate cluster due to conservation tillage and June planting during the mid-century period. In the Dilla cluster, the use of 10 t ha⁻¹ mulch, conservation tillage and early planting (February) would result in a 1.8% yield advantage compared with the control either in medium or high GHGs emission scenarios. Thus, the most promising and least risky practices among simulated strategies were the use of nitrogen and mulching in combination with tillage or planting date adjustment. However, adaptation options remained least promising and highly risky if not integrated with mulching or nitrogen use. Hence, the negative impacts of future climate change and subsequent yield gaps would be reduced by optimizing the application of nitrogen, mulch and their interaction with planting date and tillage in high and low potential areas of maize production.

Legume associations address challenges related to soil fertility and land degradation commonly encountered in maize monocultures. However, it's important to note that legumes contribute to the production of both nitrous oxide (N2O) and hydrogen ions (H+). In our examination of 693 manuscripts published between 1941 and 2023 to document the benefits of legumes in maize-based cropping systems, we identified and included 195 credible journal publications in our analysis. The findings revealed that, apart from fixing 50 to 320 kg nitrogen (N) ha-1, legumes offer various non-N advantages. When compared to maize grown after maize, a 2.4 to 173% greater yield of maize was recorded, corresponding to a 9 to 96.7% increase in biomass when maize is grown after legumes. The most substantial increases were observed in farms employing reduced tillage, residue retention, and suitable legumes. Intercropping maize with legumes, as opposed to solitary maize production, resulted in 4.3 to 80% higher biomass, 5 to 14.8% higher grain yield, and 5 to 29.5% higher profit. However, it's important to acknowledge that acid and N2O production ranged from 0.2 to 2.7 mol H+kg-1 biomass produced and 5.6 kg N2O ha-1, respectively. Implementing compatible cropping systems, increasing soil N mineralization, and recycling crop residues can enhance biological N2 fixation, reduce acid buildup in soils, mitigate N2O emissions, and simultaneously improve maize yield. In conclusion, the review underscores the necessity for location-specific cropping system standards and regulations to ensure the sustainability of maize-legume cropping systems. Key words: Cropping system, legumes, nitrous oxide, residue management, soil acidification. Legume associations address challenges related to soil fertility and land degradation commonly encountered in maize monocultures. However, it's important to note that legumes contribute to the production of both nitrous oxide (N2O) and hydrogen ions (H+). In our examination of 693 manuscripts published between 1941 and 2023 to document the benefits of legumes in maize-based cropping systems, we identified and included 195 credible journal publications in our analysis. The findings revealed that, apart from fixing 50 to 320 kg nitrogen (N) ha-1, legumes offer various non-N advantages. When compared to maize grown after maize, a 2.4 to 173% greater yield of maize was recorded, corresponding to a 9 to 96.7% increase in biomass when maize is grown after legumes. The most substantial increases were observed in farms employing reduced tillage, residue retention, and suitable legumes. Intercropping maize with legumes, as opposed to solitary maize production, resulted in 4.3 to 80% higher biomass, 5 to 14.8% higher grain yield, and 5 to 29.5% higher profit. However, it's important to acknowledge that acid and N2O production ranged from 0.2 to 2.7 mol H+kg-1 biomass produced and 5.6 kg N2O ha-1, respectively. Implementing compatible cropping systems, increasing soil N mineralization, and recycling crop residues can enhance biological N2 fixation, reduce acid buildup in soils, mitigate N2O emissions, and simultaneously improve maize yield. In conclusion, the review underscores the necessity for location-specific cropping system standards and regulations to ensure the sustainability of maize-legume cropping systems. Key words: Cropping system, legumes, nitrous oxide, residue management, soil acidification.

Quality coffee offers a wonderful taste and flavor for its consumers. However, the drivers of quality coffee were not documented fully for scaling up and further use of the practices in the study area. Hence, the present study was developed for reviewing the main factors responsible for the quality and productivity of coffee and identifying the research gaps. Preferred reporting items for systematic reviews and meta-analyses called PRISMA technique was used by critically reviewing six articles on genetics, 50 articles on environmental factors, 35 articles on management conditions, and 23 articles on socioeconomics. Results showed that people believed in a particular climate and environment as important markers of the quality of coffee; nevertheless, this research showed that established coffee management also had a crucial impact. It was also found that native agroforestry systems were necessary for high-quality coffee due to well-established relationships between shade trees, coffee plants, and farmer management as these practices alter the microclimate including the soil carbon, moisture, and nutrition regimes, allowing it to adapt to changes in evapotranspiration patterns. More significantly, coffee quality is determined by the socioeconomic factors affecting livelihoods, such as the adoption of new technologies, credit availability, farm size, additional inputs, market knowledge, physical infrastructure, extension services, literacy rate, frequency of extension visits, proximity to research centers, and producer incentives. To maintain the high standards of quality coffee production, there is a need to maintain a particular edaphic and climatic interaction, and established management systems for a particular cultivar. More importantly, infrastructural, social, and economic environments of coffee producers should be enhanced to ensure quality production.

Soil management decisions should consider physical potential of the environment, weather variability, and requirements of crops to maximize production to the potential limits. This calls for characterization of environments using selected input variables. Such studies are scanty in southern central Rift Valley of Ethiopia due to which the area is considered homogeneous and identical for agricultural planning, extension, and input delivery programs. Thus, to investigate the scenario, we employed principal component, clustering, and GIS analysis on geo-referenced physiographic and climatic attributes, and their statistical variables obtained from 43 stations with the objective of identifying homogeneous management units with similar physiography, weather pattern, and production scheduling. The analysis of principal components (PCs) indicated that three PCs explained 74.7% of variance in October, November, December, and January (ONDJ), four PCs explained 79.3% of variance in February, March, April, and May, and four PCs explained 80.5% of variance in June, July, August, and September (JJAS). Cluster-I was characterized by high altitude and low temperature in ONDJ season. Cluster-II was characterized by low altitude and high temperature across most seasons. Cluster-III was intermediate in altitude, temperature, and rainfall. Cluster-IV was characterized by high rainfall in JJAS. In all the clusters, PC1 was the mean rainfall component with strong association with altitude and longitude, while PC2 was the temperature component. PC3 is the statistical component with strong influence from mean rainfall. Thus the factors that determine the formation of clusters are reduced from 12 to 5 (T _mean, latitude, longitude, altitude, and RF_mean) and 43 stations are grouped into 4 clusters (Shamana, Bilate, Hawassa, and Dilla) which are geographically and ecologically distinct. These clusters require different sets of agro-meteorology advisory, maize management, and input delivery strategies.

Background: In southern central rift valley of Ethiopia, maize is an important crop because of its adaptation to wider agro-ecologies and higher yield potential. However, most cultivars were not parameterized to include in the database of Decision Support System for Agro-technology Transfer (DSSAT). As a result simulation of growth and yield of those cultivars was not possible under changing climate. Methods: Two set of independent crop, management and soil data were used for calibration and validation of genetic coefficients of maize cultivars (BH-540, BH-546, BH-547, Shala and Shone) under condition of historic weather (1990–2020). Later, we simulated the growth and yield of maize using twenty multimodel climate ensembles across RCP 4.5 and 8.5 during early, medium and late century across Shamana, Bilate, Hawassa and Dilla clusters using DSSATv4.8 model. Results: Cultivars BH-540, BH-546, BH-547, Shala and Shone produced yields of 5.7, 5.4, 5.2, 6.9 and 7.4 t ha ⁻¹ with the corresponding error percentage of − 0.1, − 0.8, − 1.0, − 6.1 and 2.6%. The results of normalized root mean square were 1.14–4.2 and 3.0–3.9%, for grain yield during calibration and validation, respectively showing an excellent rating. The simulation experiment produced 5.4–9.2 t ha ⁻¹ for grain yield of maize cultivars across the study areas, which is likely to fall close to 63.3% by 2070 if right adaptation options are not introduced necessitating switch in cultivars and production areas. Conclusions: There is critical need for reduction of GHGs emissions, generation of innovative adaptation strategies, and development of drought and heat stress tolerant maize cultivars. Hence, researchers and policy makers shall act with utmost urgency to embark with breeding programs that target climate change adaptation traits in maize crop.