FARM SYSTEM CHARACTERIZATION, RICE YIELD DRIVERS, FARMERS’ PERCEPTION TO CLIMATE CHANGE AND ADAPTATION, CLIMATE ANALYSIS, AND DRY SPELL MANAGEMENT OF RAINFED RICE (Oryza sativa L.) IN THE FOGERA PLAIN, NORTH-WESTERN ETHIOPIA

Abstract

Rice is one of the most important cereal crops in Fogera Plain. Despite its potential and production in Ethiopia, its productivity varies from year to year and over locations due to seasonal and intra-seasonal climate variability and household characteristics. The objectives of this study were to identify rice farm typologies, drivers of rice yield, climate change, and adaptation strategies to analyze climate variability and trend changes, and to assess dry spell management practices in the rice-based farming systems of the Fogera Plain. Rice farm typology was developed using principal component (PCA) and cluster (CA) analyses based on a survey of 230 respondents. Five farm types were identified, which were classified as (i) input based rainfed farm type 1 (FTP1), (ii) off-farm-income based farm type 2 (FTP2), (iii) irrigation-based farm type 3 (FYP3), (iv) livestock-based farm type 4 (FTP4), and (v) small and marginal rainfed farm type 5 (FTYP5). Five farm typologies that differ in their income sources and amount and livelihood sources were identified. Rice yield levels and drivers across households were summarized using descriptive statistics, the Kruskal-Wallis test, and biplot was used to rank the driving factors and determine the association of yield groups with driving factors, respectively. Four yield groups were identified. Factors affecting rice yield levels differ across yield levels. Descriptive statistics were used to assess the adaptation strategies while the multinomial logit (MNL) model was employed to identify determinants that influence the choice of adaptation options. Only 49% of the farmers' who perceive climate change applied adaptation. Farm size, credit access, wealth rank, education, extension, age, gender, farming experience, total livestock unit, input access, climate information, and family size significantly influence the choices of adaptation options. Climate change and variability analysis such as normalized rainfall anomalies index (NRAI), precipitation concentration indexes (PCI), coefficient of variation (CV), Mann-Kendall's trend test, and Sen's Slope estimator were employed. Increases in maximum and minimum temperature at all stations but decreases in annual and main season rainfall total at most of the stations were recorded. Rainfall variability and its impact on rice yield were analyzed using first-order Markov Chain Model, correlation, and multiple regression analysis. The first-order Markov Chain Model analysis revealed that dry spell lengths of 5 days (sp5), 7 days (sp7), 10 days (sp10), and 15 days (sp15) varied over the study areas with dry spells more prevalent in Woreta and Maksegnit compared to Bahir Dar and Gondar stations. Rice yield was significantly correlated with annual rainfall amounts (0.69**), length of growing period (LGP) (0.61**), and annual rainy days (0.59**), while it was negatively and significantly correlated with onset date (-0.693**) and length of a dry spell (-0.62**). Rainfall parameters explained 69% of the rice yield variability. An experiment was conducted to assess the effect of supplemental irrigation on the productivity of different rice varieties grown under rainfed conditions. The experiment was laid down in a split-plot design using water regimes, namely, dry planted rainfed rice (farmers practice) (FP), transplanted but not irrigated (TWOI), irrigation to saturation (SAT), ponding to 1 cm water (PD1), and ponding to 3 cm water (PD2 as main plot factors and five rice varieties, X-Jigna (V1), Edget (V2), Hiber (V3), Fogera-1 (V4), and Nerica-4 (V5), as subplot factors with three replications. The highest relative grain yield (105%) was obtained when X-Jigna was grown under xxi PD2 followed by V4 under PD2 (99%) and V5 under PD2 (92%). Partial Budget analysis indicated that the highest net benefit was recorded for V1 grown under PD2 while the lowest net benefit was recorded for V3 grown under TWOI. The study generally concludes that the need for designing appropriate strategies addressing inputs and credit access, market access, extension and training support, and climate information service to enhance the implementation capacity in adapting to the impacts of climate variability and change. It also indicates the need for applying appropriate dry spell management to supply optimal water management and change in planting dates can reward some of the negative impacts of climate change on rice production. The study likewise underlines the importance of farm-type and yield group-based adaptive capacity development through appropriate management options and intervention strategies to address the biophysical and socioeconomic drivers of rice farming in the Fogera Plain.