Seasonal climate forecast can inform the European agricultural sector well in advance of harvesting

Seasonal climate forecasts are a key component of sectoral climate services. Skill and reliability in predicting agro-climate indicators, co-designed with and for European wheat farmers, are here assessed. The main findings show how seasonal climate forecast provides useful information for decision-making processes in the European winter wheat-producing sector. Flowering time can be reliably predicted already at the beginning of the growing season in central and eastern Europe, thus supporting effective variety selection and timely planning of agro-management practices. The predictability of climate events relevant for winter wheat production is strongly dependent on the forecast initialization time as well as the nature of the event being predicted. Overall, regionally skillful and reliable predictions of drought events during the sensitive periods of wheat flowering and grain filling can be made already at the end of winter. On the contrary, predicting excessive wetness seems to be very challenging as no or very limited skill is estimated during the entire wheat growing season. Other approaches, e.g., linked to the use of large-scale atmospheric patterns, should be identified to enhance the predictability of those harmful events.

Systematic global evaluation of accuracy of seasonal climateforecasts for monthly precipitation of JMA/MRI-CPS2 bycomparing with a statistical system using climate indices

In this study, we aimed to evaluate the monthly precipitation forecasts of JMA/MRI-CPS2, a global dynamical seasonal climate forecast (Dyn-SCF) system operated in the Japan Meteorological Agency, by comparing them with the forecasts of a statistical SCF (St-SCF) system using climate indices systematically and globally. Accordingly, we developed a new global St-SCF system using 18 climate indices and compared the monthly precipitation of this system with those of JMA/MRI-CPS2. Consequently, it was found that JMA/MRI-CPS2 forecasts are superior to St-SCFs around the equator (10° S–10° N) even for six-month lead forecasts. For one-month lead forecasts, the accuracy of JMA/MRI-CPS2 forecasts was higher than that of St-SCFs when viewed globally. In contrast, for forecasts made two months or longer in advance, St-SCFs had an advantage in global forecasts. In addition to evaluating the accuracy of JMA/MRI-CPS2 forecasts, the slow dynamics of the ocean and atmosphere, not reproduced by the JMA/MRI-CPS2 system, were determined by comparing the evaluations, and it was concluded that this could contribute to improving Dyn-SCF systems.

Seasonal Climate Forecast Skill Assessment for the Management of Water Resources in a Run of River Hydropower System in the Poqueira River (Southern Spain)

Run of river (RoR) hydropower systems, despite being one of the most cost-effective and environmentally benign energy technologies, have the disadvantage that production is not constant because it is subject to a high variability in precipitation and snow cover. In addition, the management of RoR plants has to comply with some particular operating conditions, but also with some environmental flow requirements. This work presents the assessment of the main inputs included in a climate service, historical local data and the seasonal forecast of water inflow to RoR plants, which are used to predict the operability and the expected energy production. The analysis is presented through the application in a pilot RoR system located in the south of Spain, in a semi-arid Mediterranean area impacted by snow, where seasonal forecasting is especially challenging. The results show the high interannual variability of the operation in this kind of facilities. The outcomes indicate that seasonal climate forecast information would improve the prediction of observed river streamflow by 7.4% in reliability and 3.2% in sharpness compared to the current operational forecast based on historical data. The climate forecasts thus provide valuable information for the exploitation of available water resources, which generates a significant value for the operation of the plant and the energy production market.

Assessment of Knowledge Co-production in Adaptation to Rainfall Variability in Kitui South Sub-county, Kenya

Aims: This study tried to investigate the extent of knowledge co-production between indigenous farmers and agricultural extension in dry lands. Study Design: The study adopted survey research design where both qualitative and quantitative approaches were used. Place and Duration of Study: The study was carried out in Kitui South sub-County in the semi-arid Southeastern Kenya. Data was collected between June 2019 and August 2019. Methodology: An enumerator-administered questionnaire was used to collect data from 311 household heads. Purposive and proportional sampling techniques were used to select households which participated in the study. Data was analyzed with the aid of SPSS Version 20. Percentages and proportions were used to establish instances of knowledge co-production between indigenous and modern scientific methods of farming. Results: The study established that all households used both indigenous and scientific methods of farming except in irrigation and crop harvesting methods. The highest co-production was between use of locally preserved seeds and use of modern seasonal climate forecast (71.4%), use of traditional seasonal climate forecasts and use of modern seasonal climate (64.6%) as well as use of traditional crop storage and use modern seasonal climate forecast (59.2%). Seasonal climate forecasting was the leading corresponding method of knowledge co-production in the study area. Conclusion: The study concludes that use of both indigenous and modern methods of farming can improve adaptation to rainfall variability. The study recommends access to adequate water to promote knowledge co-production on irrigation which was lacking yet very critical in dealing with rainfall variability in the study area.

Opportunities and challenges for seasonal climate forecasts to more effectively assist smallholder farming decisions

The ability of smallholder farmers to utilise seasonal climate forecast (SCF) information in farm planning to reflect anticipated climate is a precursor to improved farm management. However, the integration of SCF by smallholder farmers into farm planning has been poor, partly because of the lack of forecast skill, lack of communication and inability to see the relevance of the SCFs for specific farming decisions. The relevance of seasonal climate forecasting in farming decisions can be enhanced through improved understanding of SCF from the smallholder farmers’ perspective. Studies that have been done of how smallholder farmers understand SCF and how the available SCFs influence smallholder farmers’ decisions are limited. Therefore, the objective of this paper was to review how smallholder farmers make decisions on farming practices based on SCFs and the challenges and opportunities thereof. The review shows that the majority of smallholder farmers in Africa make use of either scientific or indigenous knowledge climate forecasts and, in some cases, a combination of both. There are mixed results in the area of evaluating benefits of SCFs in decision-making and farm production. In some cases, the outcomes are positive, whereas in others they are difficult to quantify. Thus, the integration of SCFs into smallholder farmers’ decision-making is still a challenge. We recommend that significant work must be done to improve climate forecasts in terms of format, and spatial and temporal context in order for them to be more useful in influencing decision-making by smallholder farmers.