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

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

Analysing urban heat island patterns and simulating potential future changes

Climate change is interpreted as one of the most serious environmental problems for the 21st century. Changes in climate are now generally accepted. However, the rate of change has spatial characteristics and is highly uncertain. The Himalaya is experiencing abrupt change; so vulnerability and adaptation studies have become crucial. This pilot study presents initial findings of the research project entitled ‘Human Ecological Implications of Climate Change in the Himalaya.’ A study of climate change perceptions, vulnerability, and adaptation strategies of farming communities of the cool-wet temperate (Lumle) and the hot-wet sub-tropical (Meghauli) villages in Central Nepal was conducted. The findings are derived from the analysis of temperature and precipitation data of last 40 years, and primary data collected in September 2012. Focus Group Discussions, Key Informant Interviews, and Historical Timeline Calender were applied. The changes perceived by the communities are fairly consistent with the meteorological observations and are challenging the sustainability of social-ecological systems and communities’ livelihoods. Farming communities have adopted some strategies to minimize the vulnerability. But the adopted strategies have produced both negative and positive results. Strategies like flood control, shifting crop calendars, occupational changes and labour migrations have produced positive results in livelihood security. Occupational changes and labour migration have negatively impacted local agro-ecology and agricultural economies. Early-harvesting strategies to reduce losses from hailstorm have reduced the food and fodder security. Lack of irrigation for rice-seedlings is severely affecting the efficacy of shifting the rice-transplantation calendar. Conclusions suggest that while farmers have practiced strategies to better management of farms, livelihood sustainabilities are reaching thresholds due to the changing conditions.Rishikesh Pandey, Douglas K Bardsle

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

Comparing correction methods of RCM outputs for improving crop impact projections in the Iberian Peninsula for 21st century

Assessment of climate change impacts on crops in regions of complex orography such as the Iberian Peninsula (IP) requires climate model output which is able to describe accurately the observed climate. The high resolution of output provided by Regional Climate Models (RCMs) is expected to be a suitable tool to describe regional and local climatic features, although their simulation results may still present biases. For these reasons, we compared several post-processing methods to correct or reduce the biases of RCM simulations from the ENSEMBLES project for the IP. The bias-corrected datasets were also evaluated in terms of their applicability and consequences in improving the results of a crop model to simulate maize growth and development at two IP locations, using this crop as a reference for summer cropping systems in the region. The use of bias-corrected climate runs improved crop phenology and yield simulation overall and reduced the inter-model variability and thus the uncertainty. The number of observational stations underlying each reference observational dataset used to correct the bias affected the correction performance. Although no single technique showed to be the best one, some methods proved to be more adequate for small initial biases, while others were useful when initial biases were so large as to prevent data application for impact studies. An initial evaluation of the climate data, the bias correction/reduction method and the consequences for impact assessment would be needed to design the most robust, reduced uncertainty ensemble for a specific combination of location, crop, and crop management

Uncertainty of wheat water use: Simulated patterns and sensitivity to temperature and CO₂

Projected global warming and population growth will reduce future water availability for agriculture. Thus, it is essential to increase the efficiency in using water to ensure crop productivity. Quantifying crop water use (WU; i.e. actual evapotranspiration) is a critical step towards this goal. Here, sixteen wheat simulation models were used to quantify sources of model uncertainty and to estimate the relative changes and variability between models for simulated WU, water use efficiency (WUE, WU per unit of grain dry mass produced), transpiration efficiency (Teff, transpiration per kg of unit of grain yield dry mass produced), grain yield, crop transpiration and soil evaporation at increased temperatures and elevated atmospheric carbon dioxide concentrations ([CO2]). The greatest uncertainty in simulating water use, potential evapotranspiration, crop transpiration and soil evaporation was due to differences in how crop transpiration was modelled and accounted for 50% of the total variability among models. The simulation results for the sensitivity to temperature indicated that crop WU will decline with increasing temperature due to reduced growing seasons. The uncertainties in simulated crop WU, and in particularly due to uncertainties in simulating crop transpiration, were greater under conditions of increased temperatures and with high temperatures in combination with elevated atmospheric [CO2] concentrations. Hence the simulation of crop WU, and in particularly crop transpiration under higher temperature, needs to be improved and evaluated with field measurements before models can be used to simulate climate change impacts on future crop water demand

О перспективе извлечения йода из продукта утилизации окислителя ракетного топлива

Crop models are essential tools for assessing the threat of climate change to local and global food production. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32 degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degree C of further temperature increase and become more variable over space and time

The International Heat Stress Genotype Experiment for modeling wheat response to heat: field experiments and AgMIP-Wheat multi-model simulations

The data set contains a portion of the International Heat Stress Genotype Experiment (IHSGE) data used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat crop models and quantify the impact of heat on global wheat yield productivity. It includes two spring wheat cultivars grown during two consecutive winter cropping cycles at hot, irrigated, and low latitude sites in Mexico (Ciudad Obregon and Tlaltizapan), Egypt (Aswan), India (Dharwar), the Sudan (Wad Medani), and Bangladesh (Dinajpur). Experiments in Mexico included normal (November-December) and late (January-March) sowing dates. Data include local daily weather data, soil characteristics and initial soil conditions, crop measurements (anthesis and maturity dates, anthesis and final total above ground biomass, final grain yields and yields components), and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models

Assessing the adaptive capacity of agriculture in the Netherlands to the impacts of climate change under different market and policy scenarios (AgriAdapt project).

The AgriAdapt project has developed methodologies that enable (a) the assessment of impacts, risks and resiliencies for agriculture under changes in climatic conditions but also under changes of other drivers (market, technology, policy, etc.) and (b) the evaluation of adaptation strategies at farm type and regional scale. The methodologies are applied to arable farming over Europe and in a more integrated way, to that in Flevoland, the Netherlands as the key case. The methodologies at European level include (a) Crop modelling and (b) Market modelling. The methodologies at regional level cover the following main areas: (a) Integrated sustainability assessment, (b) Development of scenarios of farm structural change towards 2050, (c) Calculation of crop yields for different scenarios in 2050 inclusive agro-climate calendars, and (d) Partial and fully integrated analysis of farming systems in 2050, inclusive the aggregation to the regional level. Results from the application of the different methodologies are presented here. For example, exploring future farming systems shows that the most important driving factors towards 2050 within the A1-W scenario with a globalized economy, are (a) the yield increase due to climate change, (b) the expected product price change and (c) the degree of innovation in crop productivity. The effects of climate change are projected to have a positive economic effect on arable farming