Delayed chilling appears to counteract flowering advances of apricot in southern UK

Temperatures are rising across the globe, and the UK is no exception. Spring phenology of perennial fruit crops is to a large extent determined by temperature during effective chilling (endo-dormancy) and heat accumulation (eco-dormancy) periods. We used the apricot flowering records of the UK National Fruit Collections (NFC) to determine the influence of temperature trends over recent decades (1960 to 2014) on apricot (Prunus armeniaca L.) flowering time. Using Partial Least Squares (PLS) regression, we determined the respective periods for calculating chill and heat accumulation. Results suggested intervals between September 27th and February 26th and between December 31st and April 12th as the effective chilling and warming periods, respectively. Flowering time was correlated with temperature during both periods, with warming during chilling corresponding to flowering delays by 4.82 d°C-1, while warming during heat accumulation was associated with bloom advances by 9.85 d°C-1. Heat accumulation started after accumulating 62.7 ± 5.6 Chill Portions, and flowering occurred after a further 3744 ± 1538 Growing Degree Hours (above a base temperature of 4°C, with optimal growth at 26°C). When examining the time series, the increase in temperature during the chilling period did not appear to decrease overall chill accumulation during the chilling period but to delay the onset of chill accumulation and the completion of the the average chill accumulation necessary to start heat accumulation. The resulting delay in heat responsiveness appeared to weaken the phenology-advancing effect of spring warming. These processes may explain why apricot flowering time remained relatively unchanged despite significant temperature increases. A consequence of this may be a reduction of frost risk for early flowering crops such as apricot in the UK

Agroforestry Options in Northwest Vietnam

The mountainous northwest of Vietnam is home for the majority of the country’s ethnic minorities. Poverty and food insecurity are common in the region, increasing population and land scarcity have induced the expansion of agricultural areas and consequent decline of land productivity due to soil erosion and land degradation. Local farmers have begun to practice agroforestry through the introduction of high value trees into traditional cropping systems with various combinations of timber, fruit, nut forage trees and annual crops. However, because of inherent production risks and many remaining uncertainties, assessing the long-term performance of agroforestry has remained challenging. We simulated prospective system benefits of agroforestry options by developing comprehensive and holistic models that aimed to explicitly consider all relevant risks and uncertainties. The initial findings reveal model components such as drought and frost and potential extreme weather events as the primary risks to agroforestry in the region. The analysis approach is a promising tool for ex-ante assessments of other planned interventions

Data for the evaluation of irrigation development interventions in Northern Ethiopia

This data article provides the datasets that are used in the holistic ex-ante impact evaluation of an irrigation dam construction project in Northern Ethiopia [1]. We used an expert knowledge elicitation approach as a means of acquiring the data. The data shared here captures all the parameters considered important in the impact pathway (i.e. the expected benefits, costs, and risks) of the decision to construct an irrigation dam. The dataset is disaggregated for two impact pathway models: one complementing the dam construction with catchment restoration and the other without catchment restoration. Both models are scripted in the R programming language. The data can be used to examine how the construction of an irrigation dam affects the incomes as well as the food and nutritional status of farmers that are affected by the intervention

Early-spring soil warming partially offsets the enhancement of alpine grassland aboveground productivity induced by warmer growing seasons on the Qinghai-Tibetan Plateau

Aims The response of vegetation productivity to global warming is becoming a worldwide concern. While most reports on responses to warming trends are based on measured increases in air temperature, few studies have evaluated long-term variation in soil temperature and its impacts on vegetation productivity. Such impacts are especially important for high-latitude or high-altitude regions, where low temperature is recognized as the most critical limitation for plant growth

Unusually warm winter seasons may compromise the performance of current phenology models : Predicting bloom dates in young apple trees with PhenoFlex

Phenology models are crucial tools for assessing climate change impacts in forestry, ecology and agriculture. Such models are typically calibrated with observational or experimental data and validated with a set of independent observations. While there have been extensive discussions about validation approaches, systematic studies assessing the effects of the calibration data on the predictive performance of the fitted model are scarce. We evaluated the impact of marginal seasons in the calibration data set on the predictive power of an integrated modeling framework (PhenoFlex) that was recently proposed to predict spring phenology in temperate trees. We calibrated PhenoFlex with phenology records of apple trees from a multi-season experiment (59 experimental seasons) that included five unusually warm winter seasons. For comparison, we excluded these marginal seasons in a second version of the analysis. We fitted the 12 model parameters to data, assessed model performance using a common validation data set and evaluated the chill and heat responses during dormancy for both versions. Despite high overall accuracy, our results indicated a better model performance (Root Mean Square Errors of 2.3 versus 5.5 days) when excluding the marginal seasons. We observed a similar shape for the chill response curve across versions but a greater chill effectiveness when including the marginal seasons. Fitted parameters suggest a hard drop in heat efficiency beyond the optimum temperature when including the marginal seasons, probably highlighting the need for more moderate conditions during model calibration. Our results demonstrate a good performance of PhenoFlex when calibration and validation data were comparable, but they also indicate risks involved in using the framework to project phenology under conditions that differ strongly from those used for calibration. Further evaluation and validation under experimentally or naturally occurring warm conditions may improve our understanding of the response of temperate trees to mild winter conditions

Report on the main activities undertaken and preliminary findings emerging from research on the CGIAR Targeting Agricultural Innovations and Ecosystem Services in the northern Volta basin (TAI) project

The CGIAR Water, Land and Ecosystems research project on Targeting Agricultural Innovations and Ecosystem Services in the northern Volta basin (TAI) is a two year project (2014-2016) led by Bioversity International in collaboration with 11 institutes: CIAT, CIRAD, International Water Management Institute (IWMI), King’s College London (KCL), SNV World Burkina Faso (SNV), Stanford University, Stockholm Resilience Centre (SRC), University of Development Studies Ghana (UDS), University of Minnesota, University of Washington, and the World Agroforestry Institute. We are working with communities across Centre-Est Burkina Faso and Upper-East Ghana to gather empirical data, test research methodologies and co-develop knowledge on solutions to ecosystem service management challenges. Results from the project are still emerging and will continue to do so into 2017 as the team finish analysing the data and writing up their findings. This report presents the main activities accomplished and preliminary headline messages from the first 18 months of the project. Final results from the project will be made available in 2017 on the WLE website