Sensitivity of barley varieties to weather in Finland

Global climate change is predicted to shift seasonal temperature and precipitation patterns. An increasing frequency of extreme weather events such as heat waves and prolonged droughts is predicted, but there are high levels of uncertainty about the nature of local changes. Crop adaptation will be important in reducing potential damage to agriculture. Crop diversity may enhance resilience to climate variability and changes that are difficult to predict. Therefore, there has to be sufficient diversity within the set of available cultivars in response to weather parameters critical for yield formation. To determine the scale of such ‘weather response diversity’ within barley (Hordeum vulgare L.), an important crop in northern conditions, the yield responses of a wide range of modern and historical varieties were analysed according to a well-defined set of critical agro-meteorological variables. The Finnish long-term dataset of MTT Official Variety Trials was used together with historical weather records of the Finnish Meteorological Institute. The foci of the analysis were firstly to describe the general response of barley to different weather conditions and secondly to reveal the diversity among varieties in the sensitivity to each weather variable. It was established that barley yields were frequently reduced by drought or excessive rain early in the season, by high temperatures at around heading, and by accelerated temperature sum accumulation rates during periods 2 weeks before heading and between heading and yellow ripeness. Low temperatures early in the season increased yields, but frost during the first 4 weeks after sowing had no effect. After canopy establishment, higher precipitation on average resulted in higher yields. In a cultivar-specific analysis, it was found that there were differences in responses to all but three of the studied climatic variables: waterlogging and drought early in the season and temperature sum accumulation rate before heading. The results suggest that low temperatures early in the season, delayed sowing, rain 3–7 weeks after sowing, a temperature change 3–4 weeks after sowing, a high temperature sum accumulation rate from heading to yellow ripeness and high temperatures (⩾25°C) at around heading could mostly be addressed by exploiting the traits found in the range of varieties included in the present study. However, new technology and novel genetic material are needed to enable crops to withstand periods of excessive rain or drought early in the season and to enhance performance under increased temperature sum accumulation rates prior to heading

Using impact response surfaces to analyse the likelihood of impacts on crop yield under probabilistic climate change

Conventional methods of modelling impacts of future climate change on crop yields often rely on a limited selection of projections for representing uncertainties in future climate. However, large ensembles of climate projections offer an opportunity to estimate yield responses probabilistically. This study demonstrates an approach to probabilistic yield estimation using impact response surfaces (IRSs). These are constructed from a set of sensitivity simulations that explore yield responses to a wide range of changes in temperature and precipitation. Options for adaptation and different levels of future atmospheric carbon dioxide concentration [CO2] defined by representative concentration pathways (RCP4.5 and RCP8.5) were also considered. Model-based IRSs were combined with probabilistic climate projections to estimate impact likelihoods for yields of spring barley (Hordeum vulgare L.) in Finland during the 21st century. Probabilistic projections of climate for the same RCPs were overlaid on IRSs for corresponding [CO2] levels throughout the century and likelihoods of yield shortfall calculated with respect to a threshold mean yield for the baseline (1981–2010). Results suggest that cultivars combining short pre- and long post-anthesis phases together with earlier sowing dates produce the highest yields and smallest likelihoods of yield shortfall under future scenarios. Higher [CO2] levels generally compensate for yield losses due to warming under the RCPs. Yet, this does not happen fully under the more moderate warming of RCP4.5 with a weaker rise in [CO2], where there is a chance of yield shortfall throughout the century. Under the stronger warming but more rapid [CO2] increase of RCP8.5, the likelihood of yield shortfall drops to zero from mid-century onwards. Whilst the incremental IRS-based approach simplifies the temporal and cross-variable complexities of projected climate, it was found to offer a close approximation of evolving future likelihoods of yield impacts in comparison to a more conventional scenario-based approach. The IRS approach is scenario-neutral and existing plots can be used in combination with any new scenario that falls within the sensitivity range without the need to perform new runs with the impact model. A single crop model is used for demonstration, but an ensemble IRS approach could additionally capture impact model uncertainties.peerReviewe

Banse, K. and S.A. Piontkovsky (eds.). The mesoscale structure of the epipelagic ecosystem of the open Northern Arabian Sea

Book review: BANSE, K. and S.A. PIONTKOVSKY (eds.). – 2006. The mesoscale structure of the epipelagic ecosystem of the open Northern Arabian Sea. Universities Press, Hyderabad, India. 237 pp. ISBN 81 7371 496 7This book presents an extensive body of information obtained mainly from the thirtieth cruise of the R/V Professor Bodyanitsky to the Arabian Sea, carried out in 1990. It is part of a series published by the Universities Press, India, with the support of the Indian Academy of Sciences in Bangalore, whose aim is to narrow the English-Russian language gap concerning scientific literature on low-latitude oceansPeer reviewe

A crop model ensemble analysis of temperature and precipitation effects on wheat yield across a European transect using impact response surfaces

Impact response surfaces (IRSs) of spring and winter wheat yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect in Europe. The sensitivity of modelled yield to systematic increments of changes in temperature (-2 to +9°C) and precipitation (-50 to +50%) was tested by modifying values of 1981–2010 baseline weather.In spite of large differences in simulated yield responses to both baseline and changed climate between models, sites, crops and years, several common messages emerged. Ensemble average yields decline with higher temperatures (3–7% per 1°C) and decreased precipitation  (3–9% per 10% decrease), but benefit from increased precipitation (0-8% per 10% increase). Yields are more sensitive to temperature than precipitation changes at the Finnish site while sensitivities are mixed at the German and Spanish sites. Precipitation effects diminish under higher temperature changes. Inter-model variability is highest for baseline climate at the Spanish site, but relatively insensitive to changed climate. Modelled responses diverge most at the Finnish and German sites for winter wheat under temperature change. The IRS pattern of yield reliability tracks average yield levels. Inter-annual yield variability is more sensitive to precipitation than temperature, except at the Spanish site for spring wheat.Optimal temperatures for present-day cultivars are close to the baseline under Finnish conditions but below the baseline at the German and Spanish sites. This suggests that adoption of later maturing cultivars with higher temperature requirements might already be advantageous, and increasingly so under future warming

Multi-scale Modelling of Adapting European Farming Systems

European farming systems are challenged by an increasing global population, income growth, dietary changes and last, but not least, by a changing climate threatening future harvests, especially through increased frequency and severity of extreme events such as drought and heat waves. Therefore, there is a clear need to sustainably intensify and effectively adapt agricultural systems to climate change. Yet, increase in food production and adaptation are just two of many claims on agriculture, which is also supposed to meet growing demands on feed, fibre and fuel and to play a key role in mitigating climate change. The multiple claims on ecosystem services expected from agri-ecological systems call for an integrated assessment and modelling (IAM) of agricultural systems to adequately evaluate the multiple dimensions of the potential impacts as well as promising adaptation and mitigation options. This includes agriculture's responses to global change in the context of other sustainability aspects. Biophysical and socioeconomic analyses need to be integrated across different disciplines and spatiotemporal scales. In recent years the agricultural systems modelling community has made great efforts to use harmonized climate change, socio-economic and agricultural development scenarios and run them through a chain of models, e.g. by selected ensembles of biophysical and economic models at multiple scales, from farm to global. In phase 2 (2015-17) the European MACSUR knowledge hub has put its main focus on the regional (sub-national) level in the EU, with due consideration of the whole farm context. The aim of this paper is to compare three regional cases from the pool of MACSUR case studies across Europe, i.e. North Savo region in Finland, the Mostviertel region in Austria and the Oristanese region in Sardinia (Italy) representing different European farming systems along a north-south climatic gradient in Europe. These case studies represent a sample of some prominent farming systems, though only a fraction of a much larger diversity of farming and environmental conditions prevailing in Europe. We describe how adaptation options are analysed within an integrated set of linked models or model outputs combining information from different spatial scales, i.e. from region-specific crop, animal and farm level models to an analysis at regional and national level changes in agriculture and food production. First results show that adaptation to climate change affects agricultural production and farm income very differently. For some regions, e.g. in Finland there are both negative and positive effects while for the Sardinian case study adaptation to climate change have negative effects on farm income. Biophysical models, especially crop simulation models are first applied to analyse climate change impacts on yield, water use, biomass etc. and provide the outputs (i.e. delta changes) as input to economic models that contain the regional specificities of the case studies. Likewise, biophysical models are applied to analyse effects of various adaptation and mitigation options to provide information on effects of management changes on reducing damage/loss or taking opportunities from climate (adaptation) or reducing greenhouse gas emissions (mitigation). The economic models analyse economic impacts, for example the viability of management changes at farm and regional scales. Farm and regional scale economic models, backed by more detailed data and regional expert knowledge, can supply better representations of developments in each of the regions than this could be done by larger-scale (e.g. EU-wide or global) models. Sector or national economy-wide models are less specific in technical changes in agriculture, productivity changes, or in its use of inputs, due to higher level of aggregation. Nevertheless the market level view offered by sector models put the farm level changes and adaptations in a wider global context. Agricultural markets are highly integrated globally and the analyses for the case study regions also require information on global and European market developments. For example, significant changes in food demand due to changes in tastes and preferences, including aspects of climate change mitigation, may imply major changes for regional production structures. In MACSUR, this information – although not fully implemented in the case studies yet – is provided by the economic agricultural sector model CAPRI. The main strength of CAPRI in this context is that it is a global model with European focus. As such CAPRI can capture global developments and translate them to the regional level in the EU. The coupled analysis using global, EU and national level models side by side with farm level models provides unique results and much more insights on future possibilities and challenges for farmers and the food chain, than separating and restricting the analyses to either low or high aggregation level analyses. Market and policy changes often dominate longer term climate change considerations in the decision making of food chain actors, even if unfavourable weather events have become more common in recent years. Socio-economic scenarios from global to national and regional levels are needed to put adaptation and mitigation strategies in a wider context. Models, especially those that are able to accommodate biophysical, economic and policy changes are needed to show the value added from adaptations to climate change. Benefits and costs of mitigation strategies may be highly dependent on market developments. The current integrated assessment and modelling approach of MACSUR focusses on adaptation scenarios. It will be extended for the analysis and impact of mitigation policies in a later phase

Multi-scale Modelling of Adapting European Farming Systems

European farming systems are challenged by an increasing global population, income growth, dietary changes and last, but not least, by a changing climate threatening future harvests, especially through increased frequency and severity of extreme events such as drought and heat waves. Therefore, there is a clear need to sustainably intensify and effectively adapt agricultural systems to climate change. Yet, increase in food production and adaptation are just two of many claims on agriculture, which is also supposed to meet growing demands on feed, fibre and fuel and to play a key role in mitigating climate change. The multiple claims on ecosystem services expected from agri-ecological systems call for an integrated assessment and modelling (IAM) of agricultural systems to adequately evaluate the multiple dimensions of the potential impacts as well as promising adaptation and mitigation options. This includes agriculture's responses to global change in the context of other sustainability aspects. Biophysical and socioeconomic analyses need to be integrated across different disciplines and spatiotemporal scales. In recent years the agricultural systems modelling community has made great efforts to use harmonized climate change, socio-economic and agricultural development scenarios and run them through a chain of models, e.g. by selected ensembles of biophysical and economic models at multiple scales, from farm to global. In phase 2 (2015-17) the European MACSUR knowledge hub has put its main focus on the regional (sub-national) level in the EU, with due consideration of the whole farm context. The aim of this paper is to compare three regional cases from the pool of MACSUR case studies across Europe, i.e. North Savo region in Finland, the Mostviertel region in Austria and the Oristanese region in Sardinia (Italy) representing different European farming systems along a north-south climatic gradient in Europe. These case studies represent a sample of some prominent farming systems, though only a fraction of a much larger diversity of farming and environmental conditions prevailing in Europe. We describe how adaptation options are analysed within an integrated set of linked models or model outputs combining information from different spatial scales, i.e. from region-specific crop, animal and farm level models to an analysis at regional and national level changes in agriculture and food production. First results show that adaptation to climate change affects agricultural production and farm income very differently. For some regions, e.g. in Finland there are both negative and positive effects while for the Sardinian case study adaptation to climate change have negative effects on farm income. Biophysical models, especially crop simulation models are first applied to analyse climate change impacts on yield, water use, biomass etc. and provide the outputs (i.e. delta changes) as input to economic models that contain the regional specificities of the case studies. Likewise, biophysical models are applied to analyse effects of various adaptation and mitigation options to provide information on effects of management changes on reducing damage/loss or taking opportunities from climate (adaptation) or reducing greenhouse gas emissions (mitigation). The economic models analyse economic impacts, for example the viability of management changes at farm and regional scales. Farm and regional scale economic models, backed by more detailed data and regional expert knowledge, can supply better representations of developments in each of the regions than this could be done by larger-scale (e.g. EU-wide or global) models. Sector or national economy-wide models are less specific in technical changes in agriculture, productivity changes, or in its use of inputs, due to higher level of aggregation. Nevertheless the market level view offered by sector models put the farm level changes and adaptations in a wider global context. Agricultural markets are highly integrated globally and the analyses for the case study regions also require information on global and European market developments. For example, significant changes in food demand due to changes in tastes and preferences, including aspects of climate change mitigation, may imply major changes for regional production structures. In MACSUR, this information – although not fully implemented in the case studies yet – is provided by the economic agricultural sector model CAPRI. The main strength of CAPRI in this context is that it is a global model with European focus. As such CAPRI can capture global developments and translate them to the regional level in the EU. The coupled analysis using global, EU and national level models side by side with farm level models provides unique results and much more insights on future possibilities and challenges for farmers and the food chain, than separating and restricting the analyses to either low or high aggregation level analyses. Market and policy changes often dominate longer term climate change considerations in the decision making of food chain actors, even if unfavourable weather events have become more common in recent years. Socio-economic scenarios from global to national and regional levels are needed to put adaptation and mitigation strategies in a wider context. Models, especially those that are able to accommodate biophysical, economic and policy changes are needed to show the value added from adaptations to climate change. Benefits and costs of mitigation strategies may be highly dependent on market developments. The current integrated assessment and modelling approach of MACSUR focusses on adaptation scenarios. It will be extended for the analysis and impact of mitigation policies in a later phase

Can intercropping be an adaptation to drought? A model‐based analysis for pearl millet–cowpea

Cereal–legume intercropping is promoted within semi-arid regions as an adaptation strategy to water scarcity and drought for low-input systems. Our objectives were firstly to evaluate the crop model APSIM for pearl millet (Pennisetum glaucum (L.))—cowpea (Vigna unguiculata (L.) Walp) intercropping—and secondly to investigate the hypothesis that intercropping provides complimentary yield under drought conditions. The APSIM model was evaluated against data from a two year on station field experiment during the dry season of a semi-arid environment in Patancheru, India, with severe, partial and no water deficit stress (well-watered); densities of 17 and 33 plants per m−2, and intercrop and sole crop production of pearl millet and cowpea. Overall, APSIM captured the dynamics of grain yields, indicated by the Willmott Index of Agreement (IA: 1 optimal, 0 the worst) 0.91 from 36 data points (n), total biomass (IA: 0.90, n = 144), leaf area index (LAI, IA = 0.77, n = 66), plant height (IA 0.96, n = 104 pearl millet) and cowpea (IA 0.81, n = 102), as well as soil water (IA 0.73, n = 126). Model accuracy was reasonable in absolute terms (RMSE pearl millet 469 kg/ha and cowpea 322 kg/ha). However, due to low observed values (observed mean yield pearl millet 1,280 kg/ha and cowpea 555 kg/ha), the relative error was high, a known aspect for simulation accuracy in low-yielding environments. The simulation experiment compared the effect of intercropping pearl millet and cowpea versus sole cropping under different plant densities and water supplies. A key finding was that intercropping pearl millet and cowpea resulted in similar total yields to the sole pearl millet. Both sole and intercrop systems responded strongly to increasing water supply, except sole cropped cowpea, which performed relatively better under low water supply. High plant density had a consistent effect, leading to lower yields under low water supply. Higher yields were achieved under high density, but only when water supply was high: absolute highest total intercrop yields were 4,000 (high density) and 3,500 kg/ha (low density). This confirms the suitability of the common practice among farmers who use the low planting density under water scarce conditions. Overall, this study confirms that intercropping is no silver bullet, i.e. not per se a way to achieve high yield production or reduce risk under drought. It does, however, provide an opportunity to diversify food production by additionally integrating protein rich crops, such as cowpea

Analysis of rainfall variability and trends for better climate risk management in the major agro-ecological zones in Tanzania

Managing climate risk in agriculture requires a proper understanding of climatic conditions, regional and global climatic drivers, as well as major agricultural activities at the particular location of interest. Critical analyses of variability and trends in the historical climatic conditions are crucial in designing and implementing action plans to improve resilience and reduce the risks of exposure to harsh climatic conditions. However, in Tanzania, less is known about the variability and trends in the recent climatological conditions. The current study examined variability and trends in rainfall of major agroecological zones in Tanzania (1o - 12oS, 21o - 41oE) using station data from seven locations i.e. Hombolo, Igeri, Ilonga, Naliendele, Mlingano, Tumbi, and Ukiliguru which had records from 1981 to 2020 and two locations i.e. Dodoma and Tanga having records from 1958 to 2020. The variability in annual rainfall was high in Hombolo and Tanga locations (CV ≥ 28%) and low in Igeri (CV = 16%). The OND season showed the highest variability in rainfall (34% to 61%) as compared to the MAM (26% to 36%) and DJFMA (20% to 31%) seasons. We found increasing and decreasing trends in the number of rainy days in Ukiliguru and Tanga respectively, and a decreasing trend in the MAM rainfall in Mlingano. The trends in other locations were statistically insignificant. We assessed the forecast skills of seasonal rainfall forecasts issued by the Tanzania Meteorological Authority (TMA) and IGAD (Intergovernmental Authority on Development) Climate Prediction and Application Center (ICPAC). We found TMA forecasts had higher skills compared to ICPAC forecasts, however, our assessment was limited to MAM and OND seasons due to the unavailability of seasonal forecasts of the DJFMA season issued by ICPAC. Moreover, we showed that Integration of SCF with SSTa increases the reliability of the SCF to 80% at many locations which present an opportunity for better utilization of the SCF in agricultural decision making and better management of climate risks

What determines a productive winter bean-wheat genotype combination for intercropping in central Germany?

genotypes. Our study evaluates the performance of three winter wheat cultivars and eight winter faba bean genotypes (experimental inbred lines) sown as replacement row intercrops with sole cropping comparisons. Detailed agronomic, physiological and soil-based measurements were taken over three consecutive autumn-sown seasons at two sites (a marginal versus a fertile soil) in central Germany. This study aimed to contribute to our understanding of key traits required to achieve highly complementary and well-performing intercrops. Faba bean plus wheat intercrops yielded higher than sole crop equivalents at both sites, but more so at the marginal site (34 % > 12 %). High intercrop yields were associated with high wheat component yields. Such stands included faba bean genotypes that exhibited low leaf area index (LAI) values and low plant height. Tall and large faba beans i.e. with high vegetative biomass led to excessive lodging, both as a sole crop and when intercropped. To some extent, this concealed effects of faba bean genotype trait variation that would have otherwise been visible had lodging not occurred. The expression of these traits was heavily influenced by variation in environmental conditions. At the less fertile site, even tall intercropped faba beans showed relatively lower vegetative biomass, which promoted intercropped wheat and led to superior overyielding values and relative yield total. While site-specific differences are key, German winter faba beans need further genetic improvement to refrain from superfluous biomass growth when water resources are plentiful

Assessment of the relations between crop yield variability and the onset and intensity of the West African Monsoon

Timely information on the onset of rain is essential for effectively adapting to climate variability and increasing the resilience of rain-fed systems. However, defining optimal sowing dates based on the onset of rain has been challenging. We compared and analyzed the West African Monsoon onset according to Raman’s, modified Sivakumar’s, Yamada’s, and Liebmann’s definitions using station data from 13 locations in Senegal from 1981 to 2020. Subsequently, we systematically analyzed the effect of the differently estimated monsoon onsets(WAM-OS) on maize development. To this end, we applied the set of the generated WAM-OS as sowing dates in simulations of maize growth and yields, applying the Agricultural Production Systems sIMulator(APSIM) at 13 locations representing different agroclimatic regions across Senegal. We examined the impact of the sowing dates under variable conditions of soil organic carbon(SOC) and plant available water capacity(PAWC). Our analysis showed statistically significant differences between the WAM-OS dates, rainfall characteristics computed for these, and maize yields simulated using different sowing dates according to the WAM-OS definitions. We found Liebmann’s onset dates were most suitable for both hydrological and agronomic applications since they were characterized by the lowest probabilities of prolonged dry spells after onset, the highest amount of rainfall in the mid-season, and the highest simulated maize yields compared to other onset definitions. Our results highlight the importance of sowing dates and their accurate prediction for improving crop productivity in the study area. We also found SOC and PAWC were important factors that improved maize yields. We recommend improved access to climate information services to help smallholder farmers get timely information that helps them in their sowing decisions and encourage agronomic interventions that improve the SOC level, soil pore volume to retain more water and other soil properties directly(e.g., tillage) and indirectly(suited cropping systems) that contribute to enhancing crop productivity