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© 2019 Elsevier B.V. The timing of flowering phenology in most temperate trees results from the interplay of winter chilling and spring heat. As global warming progresses, reduced chilling may gain increasing importance in regulating flowering dates, and eventually offset flowering advances in response to warmer springs. Later onset of flowering events may arise, with negative effects on plant fitness. However, delayed flowering in trees may also reduce the risk from late frosts. Different temperature conditions at both margins of the apple growing areas of Shaanxi in China provide a natural laboratory to examine the responses of trees’ flowering phenology and late frost risk to climate warming. We identified the chilling and heat accumulation periods for apples by Partial Least Squares regression of first flowering dates against daily chilling and heat accumulation rates during 2001–2016. We then analyzed the impacts of temperatures during these periods on flowering timing, and evaluated the frost risk for each site. Results indicated increasing importance of chilling temperatures from north to south, with greatest effects determined for the warmest site, where delayed blossom has been observed during the past 16 years. Since late frosts mostly occurred before tree flowering, only minor frost damage was detected for our study areas, with future delays in flowering likely to reduce the frost risk even further. The redistribution of apple trees to nearby locations with cold winters, either northward or uphill, could be a promising strategy to reduce the risk of insufficient chilling and ensure that production remains viable in a warming future.

In a global warming context, analyses of historic temperature records are essential to understand the potential impacts of climate change on spring phenology. To estimate flowering trends over recent decades, we analyzed long-term temperature and phenology records of eleven local apple cultivars in Asturias (northwestern Spain) in a temperate oceanic climate. Our results show that, over a period of 30 years, bloom dates of the local cultivars have experienced relatively minor changes, considering that temperatures increased strongly since 1978, by 0.30 °C per decade. An explanation for this weak phenological response to warming may be that these temperature changes only had a small effect on overall chill accumulation, but possibly delayed the onset date of endodormancy, which may have counteracted phenology-advancing effects of warming in spring. At present, chill accumulation in this area is high, at an average of 96 Chill Portions from November to March, which indicates that chill is not currently a limiting factor for the quality of flowering and fruiting in the study area. We used Partial Least Squares (PLS) regression to delineate an effective chilling period between November 12th and February 9th and effective heat accumulation between March 15th and May 4th. While these periods appear plausible, we noticed that this approach was unable to identify well-known differences in chilling requirements among many of the cultivars, with similar chill needs determined for many of them. This observation may be explained by inaccurate expectations about cultivars’ climatic needs, by inaccuracy of the chill (and possibly heat) model or, most concerning, by inability of the PLS approach to correctly identify the chilling periods of apple cultivars in this region. Bloom dates were similarly responsive to mean temperature during the chill and the heat accumulation phases, indicating that both processes need to be considered when predicting future phenology., Funding was provided by an FPI-INIA fellowship to AD (CPD-2016-0190), AEI-MNECO through project RTA2017-00102-C03-01 and RFP2015-00022. Financial support has been provided by PRIMA, a program supported under H2020, the European Union’s Framework programme for research and innovation ("AdaMedOr" project; grant number 01DH20012 of the German Federal Ministry of Education and Research and grant number PCI2020-112113 of the Spanish Ministry of Science and Innovation)

Ornamental heather (Calluna vulgaris) production is characterized by high risks such as occurrence of fungal diseases and plant losses. Given the general absence of formal research on this economically important production system, farmers depend on their own approaches to assess plant vitality. We provide a reproducible, affordable and transparent workflow for assessing ornamental plant vitality with spectroscopy data. We use hyperspectral imaging as a non-invasive alternative for monitoring plant performance by combining the long-term experience of experts with hyperspectral images taken with a portable hyperspectral camera. We tested a custom-made setup deployed in a horticultural production facility and screened thousands of heather plants over a period of 14 weeks during their development from cuttings to young plants under production conditions. The vitality of shoots and roots was classified by experts for comparison with spectral signatures of shoot tips of healthy and stressed plants. To identify wavelengths that allow distinguishing between healthy and stressed heather plants, we evaluated the datasets using Partial Least Squares regression. Reflectance in the green (519–575 nm) and red-edge (712–718 nm) region of the spectrum was identified as most important for classifying plants as healthy or stressed. We transferred the trained Partial Least Squares regression model to independent test data obtained on a different date, correctly classifying 98.1% of the heather plants. The setup we describe here is adjustable and can be used to measure different plant species. We identify challenges in data evaluation, point out promising evaluation approaches, and make our dataset available to facilitate further studies on plant vitality in horticultural production systems.

Winter chill is expected to decrease in many mild-winter regions under future climatic conditions. Reliable estimates of the chill requirements (CR) of fruit trees are essential for assessing the current suitability of cultivars and potential climate change impacts on fruit production. We determined chill and heat requirements of ten apple cultivars in northwestern Spain using a bud-forcing method. CR ranged from 59 (‘Granny Smith’) to 90 (‘Regona’) Chill Portions (CP) according to the Dynamic Model. These results indicate that international dessert apple cultivars such as ‘Elstar’ and ‘Granny Smith’ have clearly lower CR than the studied local cultivars. The agro-climatic needs of the traditional apple cultivars are aligned with the historical climate conditions in the region. To assess future apple cultivation in northwestern Spain, we evaluated winter chill availability over the course of the twenty-first century by applying an ensemble of future climate scenarios. Relative to the past, projected winter chill might decline by between 9 and 12 CP under an intermediate global warming scenario and by between 9 and 24 CP under a pessimistic scenario. Despite relatively minor changes, the viability of some local apple cultivars may be jeopardized by their high CR. Results suggest that even a moderate decline in future winter chill, relative to fairly high levels observed in the past, can threaten the economic sustainability of fruit tree orchards composed of high-chill genotypes. Strategies such as growing low- to moderate-chill cultivars may be critical for sustaining future apple production in the region. Our findings can help guide new breeding strategies aiming to develop climate-resilient cultivars adapted to future environmental conditions.

Temperate fruit trees can enter dormancy during autumn-winter and resume active phenological development in spring in response to warm conditions. In a global warming context, recent temperature dynamics are causing changes in phenology and flowering that directly affect fruit production and yield. However, understanding how temperature regulates phenology remains a challenge. In this work, we analyzed the temperature response periods, agroclimatic requirements and sensitivity to temperature changes of 20 sweet cherry (Prunus avium L.) cultivars. We used Partial Least Squares (PLS) regression to correlate bloom dates with daily chill accumulation according to the Dynamic Model (in Chill Portions; CP) and heat accumulation according to the Growing Degree Hours model (in Growing Degree Hours; GDH) for a 20-year record from Zaragoza, Spain. The chilling periods contained several phases that clearly contributed to chill accumulation, which were disrupted by periods with no significant model coefficients. The forcing periods were reflected by consistently negative model coefficients. Chill requirements ranged from 51.6 CP to 65.2 CP, from 779 CH to 1,008 CH, and from 728 CU to 1,150 CU. The heat requirements ranged from 4,994 GDH to 7,315 GDH. Depending on the cultivar, flowering dates were determined by temperatures during both chilling and forcing phases or almost exclusively by conditions during the chilling phase. Delays of sweet cherry flowering dates appeared to arise as a response to a decrease in chill and heat accumulation by about 7 CP and about 390 GDH over the past 30 years.

Forecasting spring phenology of temperate fruit trees is of high concern for orchard plannersand fruit producers, particularly in the context of climate change. Responding to this need, horticultural researchers have developed models to estimate chill and heat requirements and project dormancy release. Despite some successes in dormancy modeling, several shortcomings still hamper reliable forecasts. Many widely used models rely on oversimplified and inflexible assumptions and are neither validated nor parameterized for most species or cultivars. More complex models are often poorly accessible due to a lack of guidance on calibration and application. Moreover, most approaches do not provide estimates of uncertainty. We aimed to develop a dormancy model that (a) is based on the best available biological understanding and experimental evidence on dormancy dynamics, (b) can flexibly adapt to species- and cultivar-specific physiology, (c) comes with a detailed description of the work-flow and (d) is open-source. The result is the new modeling framework PhenoFlex. It combines the Dynamic Model for chill accumulation with the Growing-Degree-Hours model for heat accumulation by a flexible transition. PhenoFlex is accompanied by a framework for calibrating the 12 model parameters. It is published as part of the chillR package, which contains a detailed vignette. We tested the predictive performance of PhenoFlex with 60 years of apple and pear bloom data and compared results to several benchmark models. With Root Mean Square Error values for projected bloom dates of 4.0 days for pears and 3.8 days for apples, PhenoFlex outperformed all other models including the StepChill model (10.2 and 7.7 days, respectively), and a machine learning approach (5.6 and 6.3 days). Some temperature response dynamics appeared unrealistic, indicating the need for larger training datasets with more temperature variation. We hope that PhenoFlex will facilitate further research on the temperature response dynamics of temperate tree species.

Accountability and adaptive management of recent global agreements such as the Sustainable Development Goals and Paris Climate Agreement, will in part rely on the ability to track progress toward the social and environmental targets they set. Current metrics and monitoring systems, however, are not yet up to the task. We argue that there is an imperative to consider principles of coherence (what to measure), standardization (how to measure) and decision-relevance (why to measure) when designing monitoring schemes if they are to be practical and useful. New approaches that have the potential to match the necessary scale of monitoring, with sufficient accuracy and at reasonable cost, are emerging; although, they represent a significant departure from the historical norm in some cases. Iterative review and adaptation of analytical approaches and available technology will certainly be needed to continuously design ways to best track our progress.

Governments around the world seek to create programs that will support sustainable agriculture and achieve food security, yet they are faced with uncertainty, system complexity and data scarcity when making such choices. We propose decision modeling as an innovative approach to help meet these challenges and offer a case study to show the effectiveness of the tool. We use decision analysis tools to model the possible nutrition-related outcomes of the Ugandan government's long term agricultural development plan termed ‘Vision 2040’. The analysis indicates potential shifts in household nutritional contributions through the comparison of the current small-scale diverse systems and the envisioned industrial agricultural systems that may replace them. A Monte Carlo simulation revealed that Vision 2040 plans outperform homegardens in terms of energy and some macronutrients, yet homegardens are likely to be better at producing key vitamins and micronutrients, such as Vitamin A. Value of information calculations applied to Monte Carlo outputs further revealed that gathering more data on the annual yields and nutrient contents of staples, pulses, vegetables, and fruits could improve certainty about the nutrition contribution of both scenarios. We conclude that the development of Uganda's agricultural sector should consider the role that agrobiodiversity in the current small-scale agricultural systems plays in national food and nutrition security. Any changes according to Vision 2040 should also include farmers' voices and current crop management systems as guides for a sustainable food supply in the region. This modeling approach may be a tool for governments to consider agricultural policy implications, especially given the data scarcity and agricultural variability in regions such as East Africa.

Most temperate fruit and nut trees require fulfillment of chilling and heat requirements during their dormant phase in order to flower regularly and produce economically satisfying yields. Recent and expected temperature increases are cause for concern for many orchard managers, especially in warm growing regions, because they may compromise the trees’ ability to fulfill their climatic needs. To explore temperature responses across different cultivars, we applied Partial Least Squares (PLS) regression to correlate bloom dates of 12 local and 25 foreign almond ( Prunus dulcis Mill.) cultivars in Sfax, Tunisia with daily chill and heat accumulation based on more than 30 years of phenology records from 1981 to 2014 and long-term daily minimum and maximum temperatures between 1973 and 2016. We used three chilling models (the Chilling Hours, Utah and Dynamic Models) and one forcing model (Growing Degree Hours; GDH) to quantify climatic needs. Chilling and forcing phases derived from the PLS outputs appeared discontinuous for all almond cultivars and were shorter for the local almond cultivars than for the foreign cultivars. The Dynamic Model provided the most precise estimates of chilling requirements but still appeared to have some shortcomings. According to the Chilling Hours Model, chilling needs were very low, but still higher than for the Utah Model, where the negative chill contributions by high temperatures implied negative chilling requirements. The Chilling Hours and Utah Models therefore do not seem suitable for the climate of the Sfax region. For local almond cultivars, chilling requirements were estimated at between 3.4 and 15.5 Chill Portions (CP) and heat needs between 3962 and 8873 GDH. For foreign cultivars, chilling requirements varied from 6.7 to 22.6 CP and heat needs from 2894 to 10,504 GDH. High temperatures during the chilling phase showed a significant bloom-delaying effect on most of the local and the foreign almond cultivars.

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.

Strong correlations between aboveground net primary productivity (ANPP) of grasslands and mean annual temperature or precipitation have been widely reported across regional or continental scales; however, inter-annual variation in these climate factors correlates poorly with site-specific ANPP. We hypothesize that the reason for these weak correlations is that the impacts of climatic variation on grassland productivity depend on the timing and intensity of variation in temperature and precipitation. In this study, long-term records of grassland productivity on the Loess Plateau in China were related with daily temperature and precipitation during 1992–2011 using Partial Least Squares (PLS) regression to test the above-mentioned hypothesis. Our results suggested that temperature increases during the early stage of the growing season (April–May) were positively correlated with ANPP. However, these effects were canceled out when this phase was followed by a hot and dry summer (June–July). Impacts of drought and heat in August on productivity were negligible. Increased temperature and precipitation during the senescence period (September–October) and a warmer dormancy phase (November–March) were negatively correlated with productivity in the following year, while precipitation during the dormancy period had no detectable effects. Climatic variability in summer has thus far been the dominant driver of temporal variation in grassland productivity. Warming during winter and spring currently play minor roles, but it seems likely that the importance of these secondary impacts may increase as warming trends continue. This evaluation of climate variability impacts on ecosystem function (e.g. grassland productivity) implies that not only the magnitude but also the timing of changes in temperature and precipitation determines how the impacts of climate changes on ecosystems will unfold.

Forest and Landscape Restoration (FLR) is carried out with the objective of regaining ecological functions and enhancing human well-being through intervention in degrading ecosystems. However, uncertainties and risks related to FLR make it difficult to predict long-term outcomes and inform investment plans. We applied a Stochastic Impact Evaluation framework (SIE) to simulate returns on investment in the case of FLR interventions in a degraded dry Afromontane forest while accounting for uncertainties. We ran 10,000 iterations of a Monte Carlo simulation that projected FLR outcomes over a period of 25 years. Our simulations show that investments in assisted natural regeneration, enrichment planting, exclosure establishment and soil-water conservation structures all have a greater than 77% chance of positive returns. Sensitivity analysis of these outcomes indicated that the greatest threat to positive cashflows is the time required to achieve the targeted ecological outcomes. Value of Information (VOI) analysis indicated that the biggest priority for further measurement in this case is the maturity age of exclosures at which maximum biomass accumulation is achieved. The SIE framework was effective in providing forecasts of the distribution of outcomes and highlighting critical uncertainties where further measurements can help support decision-making. This approach can be useful for informing the management and planning of similar FLR interventions.

Crop models can support agricultural decisions, yet their reliability is necessarily limited when they do not sufficiently represent the complexity and specific circumstances of the target system. In some cases, models have such prohibitively high data requirements that they are only applicable with far-reaching and often questionable assumptions. In this paper, we demonstrate a customizable solution-oriented approach for crop modelling in situations where data and resources are limited. To address system complexity and produce a probabilistic crop model that does not depend on precise data, we used participatory analysis to describe system components using individual Bayesian networks that formalize expert knowledge into probabilistic causal relationships among important variables. We then used these Bayesian networks to generate inputs for a Monte Carlo model that illustrates the determinants of crop growth and simulates plausible ranges of expected grain and biomass yields at various stages of crop development. The resulting model accounts for all important variables and their interactions, as examined by local and foreign experts and described in relevant literature. We describe how to develop and customize such a model to specific situations based on case studies related to flood-based farming systems in Ethiopia and Kenya. The model assesses the performance of cropping systems and individual crops, and identifies factors of high importance for system outcomes. This approach to crop modelling paves the way for new opportunities to support agricultural decisions, since it does not require perfect information and can accommodate system complexity and uncertainty in data-poor environments.

Available options for mitigating the impacts of extreme weather events on temperate fruit trees involve a number of risks and uncertainties, leaving growers hesitant about the benefits of implementing new technologies to protect their orchards. We used Decision Analysis approaches, which account for these risks and uncertainties, to assess investment decisions in sweet cherry production systems in central Chile. We evaluated the adoption of polyethylene covers for orchards in northern- and southern-central Chile. Gathering expert and key stakeholder knowledge, we identified relevant variables for the adoption decision and developed a causal impact pathway model. We parameterized this model by collecting estimates from experts in the form of probability distributions. We implemented the model as a Monte Carlo simulation and projected probability distributions for the Net Present Value and the annual cash flow. Results highlight that farmers in southern-central Chile could expect major benefits from covering their orchards, with a 90% confidence interval for the Net Present Value from −33,605 USD to 595,447 USD. In northern-central areas, implementing covers did not significantly improve the Net Present Value (90% confidence interval from −149,597 USD to 433,361 USD). Across zones, our model results were sensitive to market price, crop yield, and fruit quality problems (i.e. low firmness). Cover effectiveness against rain events was relevant only in the southern-central zone, whereas effectiveness against frost events was relevant in both sites. Expected Value of Perfect Information calculations revealed that additional information on yield and market price could substantially help to make a confident decision. Our simulations suggest that orchard protection in southern-central Chile may be necessary for secure and profitable cherry production in the future. In this case study, we demonstrated the applicability of Decision Analysis to support farmers in identifying appropriate and effective strategies in time to overcome future challenges resulting from climate change.

We focus on project cost, benefit and risk analysis.We propose a modelling framework that uses a hybrid and dynamic Bayesian network(BN).BN offers unique features of analysing risk scenarios and budget policies.It uses uncertainty and variability of risk and economic factors in its predictions.The framework is illustrated by a case study of agricultural development projects. Successful implementation of major projects requires careful management of uncertainty and risk. Yet such uncertainty is rarely effectively calculated when analysing project costs and benefits. This paper presents a Bayesian Network (BN) modelling framework to calculate the costs, benefits, and return on investment of a project over a specified time period, allowing for changing circumstances and trade-offs. The framework uses hybrid and dynamic BNs containing both discrete and continuous variables over multiple time stages. The BN framework calculates costs and benefits based on multiple causal factors including the effects of individual risk factors, budget deficits, and time value discounting, taking account of the parameter uncertainty of all continuous variables. The framework can serve as the basis for various project management assessments and is illustrated using a case study of an agricultural development project.

Agroforestry has attracted considerable attention in recent years because of its potential to reduce poverty, improve food security, reduce land degradation and mitigate climate change. However, progress in promoting agroforestry is held back because decision-makers lack reliable tools to accurately predict yields from tree-crop mixtures. Amongst the key challenges faced in developing such tools are the complexity of agroforestry, including interactions between various system components, and the large spatial domains and timescales over which trees and crops interact. A model that is flexible enough to simulate any agroforestry system globally should be able to address competition and complementarity above and below ground between trees and crops for light, water and nutrients. Most agroforestry practices produce multiple products including food, fiber and fuel, as well as income, shade and other ecosystem services, all of which need to be simulated for a comprehensive understanding of the overall system to emerge. Several agroforestry models and model families have been developed, including SCUAF, HyPAR, Hi-SAFE/Yield-SAFE and WaNuLCAS, but as of 2015 their use has remained limited for reasons including insufficient flexibility, restricted ability to simulate interactions, extensive parameterization needs or lack of model maintenance. An efficient approach to improving the flexibility and durability of agroforestry models is to integrate them into a well-established modular crop modeling framework like APSIM. This framework currently focuses on field-scale crops and pastures, but has the capability to reuse or interoperate with existing models including tree, livestock and landscape models, it uses parameters that are intuitive and relatively easy to measure, and it allows scenario analysis that can include farm-scale economics. Various types of agroforestry systems are currently being promoted in many contexts, and the impacts of these innovations are often unclear. Rapid progress in reliable modeling of tree and crop performance for such systems is needed to ensure that agroforestry fulfills its potential to contribute to reducing poverty, improving food security and fostering sustainability.

Agricultural management decisions are usually made without perfect knowledge. Decision Analysis (DA) approaches translate available uncertain information on costs, benefits and risks involved in decisions into actionable management recommendations. We illustrate the use of DA procedures to inform decisions on disease management strategies in ornamental plant production. We worked with heather growers and other stakeholders in North Rhine-Westphalia, Germany, to model the impacts of changing disease management practices and to generate comprehensive forecasts of net returns. Through sensitivity analysis and Value of Information assessment we identified critical uncertainties regarding the feasibility of improved practices. Partial Farm Budgets for decision options ranged from a loss of more than 200,000 € to a gain of nearly 70,000 € per hectare and year. Findings suggest that reducing pesticide applications without additional monitoring may substantially increase production risks (chance of loss of 76%) and that intensified plant monitoring is likely to increase net benefits (chance of gain of 68%) by allowing earlier detection and more focused fungicide application. Our Decision Analysis approach facilitated ex-ante evaluation of innovative management strategies in heather production, and it holds promise for similar evaluations in other agricultural production systems.

Winter chill, which temperate trees require in order to overcome dormancy, is expected to decrease substantially in the future in most deciduous fruit tree growing areas. Several mathematical models have been developed in different regions to quantify chill requirements of tree species and cultivars. The Dynamic model has emerged as the most plausible and reliable model, yet all chill models have been found inadequate in at least some growing regions. Accurate models are crucial for the development of quantitatively appropriate climate change adaptation strategies for temperate orchards. To demonstrate the importance of model choice we compared the outputs from 13 agricultural and forest chill models using past and projected future weather data for nine sites in Chile, Tunisia and Germany. To evaluate chill risk, we used a weather generator calibrated with 45 years of temperature data to generate 100 years of synthetic temperature records per scenario for multiple climate scenarios. Chill was computed for 10 past scenarios and projected for 60 future scenarios (for 2050 and 2085 according to greenhouse gas concentration scenarios RCP4.5 and RCP8.5, using projections from 15 climate models). Results show that estimations differ substantially across chill models, even for the same sites and scenarios. The “Chilling Hours” model and the “Chilling Rate” function showed high sensitivity across regions in future scenarios. The “North Carolina”, “Utah”, “Modified Utah” and “Low Chill” models all suggest negative chill levels for past and future scenarios in Tunisia (despite the thriving fruit tree industry there). Only two models projected chill decreases in all sites. In Mediterranean climate areas (central Chile and Tunisia) the “Dynamic” and “Positive Utah” models forecasted similar chill reductions for future scenarios, whereas in temperate locations (Germany) the “Dynamic” model forecasted lower chill increase compared with the “Utah” and “Positive Utah” models. Despite the “Dynamic” and the “Positive Utah” models showing similar performance among climates, the “Dynamic” model appears to be the best current option, due its more physiologically credible structure. However, further research is needed to develop or identify models that are valid across wide climatic gradients. Our results show that a major source of variation and inaccuracy in chilling assessments is the choice of the chill model used to make the assessment.

Maize yield productivity in Ethiopia has been below the genetic potential—constrained, among other factors, by frequent moisture stress due to local weather variability. Changes in climate may exacerbate these limitations to productivity, but current research on projecting responses of maize yields to climate change in Ethiopia is inadequate. The research objectives of this project were to (1) calibrate and evaluate the performance of the APSIM-maize and DSSAT CSM-CERES-Maize models, and (2) assess the impact of climate change on future maize yield. The climate periods considered were near future (2010–2039), middle (2040–2069) and end of the 21st century (2070–2099). Climate simulations were conducted using 20 General Circulation Models (GCMs) and two Representative Concentration Pathways (RCPs; RCP4.5 and RCP8.5). Both crop models reasonably reproduced observations for time to anthesis, time to physiological maturity and crop yields, with values for the index of agreement of 0.86, 0.80 and 0.77 for DSSAT, and 0.50, 0.89 and 0.60 for APSIM. Similarly root mean square errors were moderate for days to anthesis (1.3 and 3.7 days, for DSSAT and APSIM, respectively), maturity (4.5 and 3.1 days), and yield (1.1 and 1.2 tons). Deviations of simulated from observed values were low for days to anthesis (DSSAT: −2.4–2.3%; APSIM: 0–6%) and days to maturity (DSSAT: −0.6–4.4%; APSIM: −1.9–3.3%) but relatively high for yield (DSSAT: −18.5–21.2%; APSIM: −19.1–37.1%). Overall the goodness-of-fit measures indicated that models were useful for assessing maize yield at the study site. Simulations for future climate scenarios projected slight increases in the median yield for the near future (1.7%–2.9% across models and RCPs), with uncertainty increasing toward mid-century (0.6–4.2%). By the end of the 21st century, projections ranged between yield decreases by 6.3% and increases by 4%. Differences between the RCPs were small, probably due to factor interactions, such as higher temperatures reducing the CO2-induced yield gains for the higher RCP. Uncertainties in studies on the impact of climate change on maize might arise mostly from the choice of crop model and GCM. Therefore, the use of multiple crop models along with multiple GCMs would be advisable in order to adequately consider uncertainties about future climate and crop responses and to provide comprehensive information to policy makers and planners. Overall, results of this study (based on two different crop simulation models across 20 GCMs, and two RCPs under similar crop management) consistently indicated a slight increase in yield.

Instead of the commonly used approach of conducting controlled experiments to estimate chilling and heat requirements (CR and HR) of fruit trees, the statistical method of Partial Least Squares (PLS) regression was applied to identify the CR and HR of apricot (Prunus armeniaca L.) in Beijing, China by correlating first flowering dates of apricot with daily chilling and heat accumulation during 1963-2010. Three common chilling models (the 0-7.2 degrees C, Utah and Dynamic Models) and one forcing model (the Growing Degree Hour Model) were used to convert daily temperature data into daily chill and heat accumulation rates. The results indicated that PLS regression analysis is a useful approach to estimate the CR and HR of fruit trees wherever phenology and climate observations have been conducted for long periods. Use of all chilling models indicated similar chilling periods for apricot in Beijing (mid-September to early March), while the identified forcing period started in early January and extended to the first flowering date for each year. The Dynamic Model appeared to be the most accurate model with smallest year-to-year variation in chill accumulated during the chilling period (coefficient of variation of only 7.5%). Using the Dynamic Model for chill, and the Growing Degree Hour Model for heat quantification, the CR of apricot in Beijing was determined at 75 6 Chill Portions (CP) and the HR at 3055 938 Growing Degree Hours (GDH). (C) 2015 Elsevier B.V. All rights reserved.

Study Region: The Upper catchments of the Nzoia River basin in western Kenya. Study Focus: The potential streamflow responses to climate change in the upper Nzoia River basin are studied. The Soil and Water Assessment Tool (SWAT) was forced with monthly temperature and precipitation change scenarios for the periods 2011–2040 (2020s), 2041–2070 (2050s) and 2071–2100 (2080s). Data from 10 climate models and three greenhouse gases emission scenarios was downscaled using the delta change method and used in the SWAT model. Streamflow data for the periods 1986–1998 and 1973–1985 was used for model calibration and validation respectively. New Hydrological Insights for the Region: Comparison between the simulated baseline and future streamflow shows that in the Koitobos and Kimilili watersheds, August to December streamflow is likely to be highly altered. In the Kuywa watershed, March to June flows is likely to change considerably due to climate change. Major streamflow changes are likely in March to June and August to November in the Rongai watershed. Projected changes differed between the four watersheds despite their proximity, indicating different sensitivities to climate change and uncertainty about the potential hydrological impacts of climate change in the area. Keywords: Climate change, Streamflow, Nzoia basin, Mt. Elgon, SWAT

The timing of spring phenology in most temperate zone plants results from the combined effects of both autumn/winter cold and spring heat. Temperature increases in spring can advance spring phases, but warming in autumn and winter may slow the fulfilment of chilling requirements and lead to later onset of spring events, as evidenced by recent phenology delays in response to warming at some locations. As warming continues, the phenology-delaying impacts of higher autumn/winter temperatures may increase in importance, and could eventually attenuate - or even reverse - the phenology-advancing effect of warming springs that has dominated plant responses to climate change so far. To test this hypothesis, we evaluated the temperature responses of apricot bloom at five climatically contrasting sites in China. Long-term records of first flowering dates were related to temperature data at daily resolution, and chilling and forcing periods were identified by Partial Least Squares (PLS) regression of bloom dates against daily chill and heat accumulation rates. We then analyzed the impacts of temperature variation during the chilling and forcing periods on tree flowering dates for each site. Results indicated that in cold climates, spring timing of apricots is almost entirely determined by forcing conditions, with warmer springs leading to earlier bloom. However, for apricots at warmer locations, chilling temperatures were the main driver of bloom timing, implying that further warming in winter might cause delayed spring phases. As global warming progresses, current trends of advancing phenology might slow or even turn into delays for increasing numbers of temperate species. (C) 2014 The Authors. Published by Elsevier B.V.

The tree rhododendrons include the most widely distributed Himalayan Rhododendron species belonging to the subsection Arborea. Distributions of two members of this sub-species were modelled using bioclimatic data for current conditions (1950–2000). A subset of the least correlated bioclimatic variables was used for ecological niche modelling (ENM). We used an ENM ensemble method in the BiodiversityR R-package to map the suitable climatic space for tree rhododendrons based on 217 point location records. Ensemble bioclimatic models for tree rhododendrons had high predictive power with bioclimatic variables, which also separated the climatic spaces for the two species. Tree rhododendrons were found occurring in a wide range of climate and the distributional limits were associated with isothermality, temperature ranges, temperature of the wettest quarter, and precipitation of the warmest quarter of the year. The most suitable climatic space for tree rhododendrons was predicted to be in western Yunnan, China, with suitability declining towards the west and east. Its occurrence in a wide range of climatic settings with highly dissected habitats speaks to the adaptive capacity of the species, which might open up future options for their conservation planning in regions where they are listed as threatened.

Development programs have typically neglected uncertainty and variability in terms of outcomes and socio-ecological context when promoting conservation agriculture (CA) throughout sub-Saharan Africa. We developed a simple Monte Carlo-based decision model, calibrated to global data-sets and parameterized to local conditions, to predict the range of yield benefits farmers may obtain when adopting CA in two ongoing agricultural development projects in East Africa. Our general model predicts the yield effects of adopting CA-related practices average −0.60 ± 2.05 (sd) Mg maize ha−1 year−1, indicating a near equal chance of positive and negative impacts on yield. When using site-specific, socio-economic, and biophysical data, mean changes in yield were more negative (−1.29 and −1.34 Mg ha−1 year−1). Moreover, practically the entire distributions of potential yield impacts were negative suggesting CA is highly unlikely to generate yield benefits for farmers in the two locations. Despite comparable aggregate effects at both sites, factors such as land tenure, access to information, and livestock pressure contrast sharply highlighting the need to quantify the range of livelihood and landscape effects when evaluating the suitability of the technology. This analysis illustrates the potential of incorporating uncertainty in rapid assessments of agricultural development interventions. Whereas this study examines project-level decisions on one specific intervention, the approach is equally relevant to address decision-making for multiple interventions, at multiple scales, and for multiple criteria (e.g., across ecosystem services), and thus is an important tool that can support linking knowledge with action.

Social actors can strongly affect the sustainability of agricultural operations by influencing farmers’ decisions and choices. Such actors include: (1) loss-making investors who abandon farms due to low returns, (2) angry neighbours negatively affected by farming operations and engaging in silent or active conflict, (3) dissatisfied customers at the end of the value chain who reject the products and shift to alternative providers, and (4) overacting regulators who over-regulate farm activities. A higher order sustainability concept considers the ability of farms to adapt and learn from early signs of threats. A number of response paths based on policies, incentives and information supply have been developed to support learning and adjustments. Emphasis on the nested-scales relations of incremental sustainability and sustainagility, in addition to the more commonly articulated ecological threshold perspective, helps identify key indicators that characterize unsustainability processes across countries and contexts. A dynamic systems understanding also assists selection of process indicators focused on response paths that complement result-oriented approaches in current sustainability assessment frameworks.

Trees inside and outside forests contribute to food security in Africa in the face of climate variability and change. They also provide environmental and social benefits as part of farming livelihoods. Varied ecological and socio-economic conditions have given rise to specific forms of agroforestry in different parts of Africa. Policies that institutionally segregate forest from agriculture miss opportunities for synergy at landscape scale. More explicit inclusion of agroforestry and the integration of agriculture and forestry agendas in global initiatives on climate change adaptation and mitigation can increase their effectiveness. We identify research gaps and overarching research questions for the contributions in this special issue that may help shape current opinion in environmental sustainability.

Agroforestry systems are complex assemblages of ecosystem components, each of which responds to climate. Whereas climate change impacts on crops grown in monocultures can reasonably well be projected with process-based crop models, robust models for complex agroforestry systems are not available. Yet impact projections are needed because of the long planning horizons required for adequate management of tree-based ecosystems. This article explores available options for projecting climate change impacts on agroforestry systems, including the development of process-based models, species distribution modeling, climate analogue analysis and field testing in climate analogue locations. Challenges and opportunities of each approach are discussed.

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. Keywords: Ex-ante impact assessment, Feasibility study, Decision support, Cost benefit analysis, Water harvesting, Dam construction

Temperate-zone trees must fulfill cultivar-specific chilling and heat requirements during the dormant period, in order to produce leaves and flowers in the following growing season. Timing and accumulation rate of chill and heat are understood to determine the timing of spring events, but both processes are difficult to observe in dormant tree buds. Where long-term phenological observations are available, Partial Least Squares (PLS) regression offers a statistical opportunity to delineate phases of chill and heat accumulation and determine the climatic requirements of trees. This study uses PLS regression to explore how the timing of spring events of chestnut in China, cherry in Germany and walnut in California is related to variation in the daily rates of chill and heat accumulation, as calculated with horticultural models. Dependent variables were 39 years of flowering dates for chestnuts in Beijing (China), 25 years of cherry bloom in Klein-Altendorf (Germany) and 54 years of walnut leaf emergence in Davis (California, USA). These were related to daily accumulation rates of chill, calculated with the Dynamic Model, and heat, calculated with the Growing Degree Hours Model. Compared to an earlier version of the procedure, in which phenological dates were related to unprocessed temperature data, delineation of chilling and forcing phases was much clearer when using horticultural metrics to quantify chill and heat. Chestnut bloom in the cold-winter climate of Beijing was found to depend primarily on the rate of heat accumulation, while cherry bloom in the temperate climate of Germany showed dependence on both chill and heat accumulation rates. The timing of walnut leaf emergence in the mild-winter climate of California depended much more strongly on chill accumulation rates. Chilling (in Chill Portions = CP) and heat (in Growing Degree Hours = GDH) requirements determined based on PLS regression were 79.8 +/- 5.3 CP and 13,466 +/- 1918 GDH for chestnut bloom in Beijing, 104.2 +/- 8.9 CP and 2698 +/- 1183 GDH for cherry bloom in Germany, and 37.5 +/- 5.0 CP and 11,245 +/- 1697 GDH for walnut leaf emergence in California. Spring phases of cherry in Klein-Altendorf and especially chestnut in Beijing will likely continue to advance in response to global warming, while for walnut in California, inadequate chilling may cause delays in flowering and leaf emergence. Such delays could serve as an early-warning indicator that future productivity may be threatened by climate change. The R package 'chillR' makes the method used in this study available for wider use. (C) 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Climate change has affected the phenology of plants and animals throughout the world, but few studies have evaluated climate responses of fruit trees in East Asia. In particular, the response of tree phenology to warming during different parts of the year has not been explored. We evaluated long-term records (1963-2008) of chestnut (Castanea mollissima Blume) first flowering, leaf coloring and length of the growing season from Beijing, China. Phenological dates were related with daily temperatures (subjected to an 11-day running mean) for the 12 months leading up to the respective events, using Partial Least Squares (PLS) regression. For each phenological indicator, regression results identified two relevant phases, during which temperatures were correlated with event timing or growing season length.

The under-ground mushroom Tuber indicum is renowned for its economic, nutritional, ethnobotanical and ecological importance. For the development of sustainable harvest and conservation practices, better knowledge about the mushroom's habitat is indispensable. However, few approaches allow monitoring T. indicum's distribution on a large geographic scale. Apart from the difficulty to directly monitor them by Remote Sensing and GIS technology, a particular challenge arises from the sampling limitations for this seasonal mushroom. This problem is common in geology, where underground mineral resources must be mapped without direct observations. Geologists apply the 'Weights of Evidence' method for such situations, and this approach may have potential for underground mushrooms as well. We thus constructed potential habitat maps for T. indicum using the Weights of Evidence method. Based on field survey and published sources, ten influential indicators associated with T. indicum were selected and mapped for Longyang district in southwestern Yunnan, China. Two predictive models were established from independent environmental layers. In order to build better understanding of the models' predictive ability, apart from the Receiver Operating Characteristic (ROC) curve, the Area Adjusted Frequency (AAF) approach was also applied for model evaluation. For the final map, the best-performing model was selected. The resulting habitat map could provide guidance for future conservation activities. (C) 2012 Elsevier B.V. All rights reserved.

Temperate fruit and nut species require exposure to chilling conditions in winter to break dormancy and produce high yields. Adequate winter chill is an important site characteristic for commercial orchard operations, and quantifying chill is crucial for orchard management. Climate change may impact winter chill. With a view to adapting orchards to climate change, this review assesses the state of knowledge in modelling winter chill and the performance of various modelling approaches. It then goes on to present assessments of past and projected future changes in winter chill for fruit growing regions and discusses potential adaptation strategies. Some of the most common approaches to modelling chill, in particular the Chilling Hours approach, are very sensitive to temperature increases, and have also been found to perform poorly, especially in warm growing regions. The Dynamic Model offers a more complex but also more accurate alternative, and use of this model is recommended. Chill changes projected with the Dynamic Model are typically much less severe than those estimated with other models. Nevertheless, projections of future chill consistently indicate substantial losses for the warmest growing regions, while temperate regions will experience relatively little change, and cold regions may even see chill increases. Growers can adapt to lower chill by introducing low-chill cultivars, by influencing orchard microclimates and by applying rest-breaking chemicals. Given substantial knowledge gaps in tree dormancy, accurate models are still a long way off. Since timely adaptation is essential for growers of long-lived high-value perennials, alternative ways of adaptation planning are needed. Climate analogues, which are present-day manifestations of future projected climates, can be used for identifying and testing future-adapted species and cultivars. Horticultural researchers and practitioners should work towards the development and widespread adoption of better chill accumulation and dormancy models, for facilitating quantitatively appropriate adaptation planning.

Many biological processes produce only one quantitative outcome per year, resulting from temperatures and precipitation during hundreds of days leading up to the event. Traditional regression approaches incur problems in such a setting, because independent variables are highly autocorrelated and their number often greatly exceeds the number of observations. Partial Least Squares Regression (PLS), a statistical analysis tool developed to handle these situations and widely used in hyperspectral remote sensing, was tested for its usefulness for explaining the climate responses of biological processes, using walnut phenology in California as an example. Observations of first female bloom, first male bloom and leaf emergence of three walnut cultivars at Davis, CA were coupled with daily temperature data since 1951. The dataset was analyzed by PLS, using three temperature inputs: (1) daily mean temperatures, (2) 11-day running means of daily mean temperatures and (3) monthly mean temperatures. For all data constellations, the Variable-Importance-in-the-Projection (VIP) statistic indicated a number of periods, during which temperatures were important determinants of phenological events, and the model-coefficients-of-the-centered-and-scaled-data (MC) statistic showed the direction, in which high temperatures during these phases influenced walnut flowering and leaf emergence. In all analyses, a delaying effect of warm winters, and an advancing effect of warm springs were clearly visible. It was also possible to identify the transition between the chilling and forcing phases, and the VIP and MC plots indicated quantitative differences in the effectiveness of winter chill during different phases of the dormancy season. Such effects have not been captured in any phenology models currently applied to fruit trees, indicating that PLS has potential to help refine such models. PLS can also be used for guiding experimental research by pinpointing the parts of the season that are most important for the timing of budburst. Results suggested that more than 20 years of observed data were necessary for producing clearly recognizable temperature response patterns, limiting the applicability of PLS to long time series.

Estimating groundwater recharge in response to increased atmospheric CO2 concentration and climate change is critical for future management of agricultural water resources in arid or semi-arid regions. Based on climate projections from the Intergovernmental Panel on Climate Change, this study quantified groundwater recharge under irrigated agriculture in response to variations of atmospheric CO2 concentrations (550 and 970 ppm) and average daily temperature (+1.1 and +6.4 °C compared to current conditions). HYDRUS 1D, a model used to simulate water movement in unsaturated, partially saturated, or fully saturated porous media, was used to simulate the impact of climate change on vadose zone hydrologic processes and groundwater recharge for three typical crop sites (alfalfa, almonds and tomatoes) in the San Joaquin watershed in California. Plant growth with the consideration of elevated atmospheric CO2 concentration was simulated using the heat unit theory. A modified version of the Penman�Monteith equation was used to account for the effects of elevated atmospheric CO2 concentration. Irrigation amount and timing was based on crop potential evapotranspiration. The results of this study suggest that increases in atmospheric CO2 and average daily temperature may have significant effects on groundwater recharge. Increasing temperature caused a temporal shift in plant growth patterns and redistributed evapotranspiration and irrigation water use earlier in the growing season resulting in a decrease in groundwater recharge under alfalfa and almonds and an increase under tomatoes. Elevating atmospheric CO2 concentrations generally decreased groundwater recharge for all crops due to decreased evapotranspiration resulting in decreased irrigation water use. Increasing average daily temperature by 1.1 and 6.4 °C and atmospheric CO2 concentration to 550 and 970 ppm led to a decrease in cumulative groundwater recharge for most scenarios. Overall, the results indicate that groundwater recharge may be very sensitive to potential future climate changes

Many fruit and nut species require cold temperatures during the dormancy season to initiate flowering and bear fruit. Quantifying these chilling requirements is crucial for identifying appropriate cultivars for a given site, for timing applications of rest-breaking chemicals and for predicting consequences of climate change. We present a new method to test temperature models describing chilling and heat requirements of perennial plants, and use this method to compare the ability of four chilling models (Chilling Hours, Utah Model, Positive Utah Model and Dynamic Model) to explain walnut phenology in California. When plotting remaining heat before a phenological stage is reached against accumulated winter chill, observational curves for all years should intersect in one common point, assuming fixed chilling and heat requirements and a sequential fulfillment of these requirements. This point defines the chilling and forcing requirements of the plant, and the quality of the chilling/heat model combination is indicated by how well defined the intersection point is. We used this method on a total of 1297 phenological observations, including four walnut cultivars, seven phenological stages and eight locations in California. Using an hourly temperature record, winter chill was quantified by the four chilling models and remaining heat was estimated using the Growing Degree Hour concept. The theoretical intersection point was more clearly defined for the Dynamic and Positive Utah Models than for the Chilling Hours and Utah Models in almost all cases, indicating that these are superior in explaining walnut phenology. It was also apparent that chilling models were not equivalent and that chilling requirements determined under constant temperature conditions, when quantified in Chilling Hours, were not representative of chilling requirements in orchards.

Many fruit and nut crops require cold temperatures in winter to break dormancy. Quantifying this chilling requirement and selecting appropriate cultivars for the climate of a growing region is crucial for successful cultivation of such crops. Several models exist to quantify winter chill, and each growing region uses a model that has been shown to perform well under local climatic conditions. We tested the sensitivity of four commonly used chilling models to projected climatic change likely to affect fruit and nut production in the near future. For six sites in California's Central Valley, we generated 100 years of synthetic hourly weather records, representing climatic conditions in 1950, 2000 and projected temperatures in 2041–2060 derived from three IPCC-AR4 General Circulation Models (GCMs; CSIRO, HadCM3 and MIROC; A2 greenhouse gas emissions scenario). Mean winter chill for each site and year was calculated using the Chilling Hours, Utah, Positive Utah and Dynamic models. All chilling models predicted substantial decreases in winter chill at all sites, but the extent of these decreases varied depending on the model used. Across all sites between 1950 and 2050, mean chilling was predicted to decrease by 33% (Chilling Hours), 26% (Utah Model), 16% (Dynamic Model) and 14% (Positive Utah Model). Research efforts are needed to identify the most appropriate chilling model for preparing fruit and nut growers for the imminent effects of climate change.

Remote sensing techniques can decrease pest monitoring costs in orchards. To evaluate the feasibility of detecting spider mite damage in orchards, we measured visible and near infrared reflectance of 1153 leaves and 392 canopies in 11 peach orchards in California. Pairs of significant wavelengths, identified by Partial Least Squares regression, were combined into normalized difference indices. These and 9 previously published indices were evaluated for correlation with mite damage. Eight spectral regions for leaves and two regions for canopies (at blue and red wavelengths) were significantly correlated with mite damage. These findings were tested by calculating normalized difference indices from the Red and Blue bands of six multispectral aerial images. Index values were linearly correlated with mite damage (R2 = 0.47), allowing identification of mite hotspots in orchards. However, better standardization of aerial imagery and accounting for perturbing environmental factors will be necessary for making this technique applicable for early mite detection.

Summary Quantifying the hydrological response to an increased atmospheric CO 2 concentration and climate change is critical for the proper management of water resources within agricultural systems. This study modeled the hydrological responses to variations of atmospheric CO 2 (550 and 970 ppm), temperature (+1.1 and +6.4 °C), and precipitation (0%, ±10%, and ±20%) based on Intergovernmental Panel on Climate Change projections. The Soil and Water Assessment Tool (SWAT) was used to model the hydrology and impact of climate change in the highly agricultural San Joaquin watershed in California. This watershed has an area of 14,983 km 2 with a Mediterranean climate, resulting in a strong dependence on irrigation. Model calibration (1992–1997) and validation (1998–2005) resulted in Nash–Sutcliffe coefficients of 0.95 and 0.94, respectively, for monthly stream flow. The results of this study suggest that atmospheric CO 2 , temperature and precipitation change have significant effects on water yield, evapotranspiration, irrigation water use, and stream flow. Increasing CO 2 concentration to 970 ppm and temperature by 6.4 °C caused watershed-wide average evapotranspiration, averaged over 50 simulated years, to decrease by 37.5%, resulting in increases of water yield by 36.5%, and stream flow by 23.5% compared to the present-day climate. Increasing temperature caused a temporal shift in plant growth patterns and redistributed evapotranspiration and irrigation water demand earlier in the year. This caused an increase in stream flow during the summer months due to decreased irrigation demand. Water yield, however, decreased with an increase in temperature. Increase of precipitation by ±10% and ±20% generally changed water yield and stream flow proportionally, and had negligible effects on predicted evapotranspiration and irrigation water use. Overall, the results indicate that the San Joaquin watershed hydrology is very sensitive to potential future climate changes. Agricultural implications include changes to plant growth rates, irrigation timing and runoff, all of which may affect future water resources and water quality.

In the desert country of Oman, available water resources are scarce and scattered. In most locations where water can be accessed, this resource is harnessed by oases planted to date palm (Phoenix dactylifera L.) and other crops. So far, little is known about the site-specific conditions determining the existence, size and type of these oases. Remote sensing and image processing techniques were used to locate oases, to characterize the sites according to their topographic, hydrologic and geologic characteristics and to develop a typology of oases in northern Oman. To derive oasis positions, we calculated the Normalized Difference Vegetation Index (NDVI) of Landsat images covering all of northern Oman, subtracted a regional average NDVI, averaged the resulting grid over 3 × 3 pixels and extracted the brightest of five classes determined by a natural breaks algorithm. A buffer of six pixels was added to the oases and the vegetated area as determined by the NDVI was summarized for these polygons. The oasis detection procedure was validated using Google Earth Pro®. Topographic information was derived from data of the Shuttle Radar Topography Mission (SRTM), complemented by digitized Russian military maps, from which mean elevations and elevation range above the oases within a buffer of 2 km were extracted. Water contributing upslope area and distance to streams with catchments of 10 km2 and 100 km2 were derived from the same elevation model. All geologic formations of northern Oman were assigned to one of 7 groups and tested for influence on vegetation surrounding them. Four such geologic settings were identified and described by categorical variables. All input parameters were used to define oasis types based on cluster analysis. Our algorithm detected 2663 oases in northern Oman, of which 2428 had vegetated areas of more than 0.4 ha, the minimum size for reliable detection. The oases were subdivided into six groups. ‘Plain Oases’ (49% of all oases) lie mostly in the plains east and west of the mountains, and are fed by groundwater flow in Quaternary sediments. ‘Foothill Oases’ (46%) are scattered over the foothills, where they draw their water from groundwater flow that is channeled by rock formations. ‘Mountain Oases’ (3%) and ‘Kawr Oases’ (0.5%) lie in the mountains, close to an unconform boundary between limestones and confining rocks. ‘Drainage Oases’ (0.3%) are the largest oases in northern Oman. They lie close to a drainage channel, which drains the entire area west of the mountains. Finally, ‘Urban Oases’ (1.7%) consist of parks and sporting facilities, which do not lie in conclusive hydrologic settings.

The Digital Elevation Model (DEM) derived from NASA's Shuttle Radar Topography Mission is the most accurate near-global elevation model that is publicly available. However, it contains many data voids, mostly in mountainous terrain. This problem is particularly severe in the rugged Oman Mountains. This study presents a method to fill these voids using a fill surface derived from Russian military maps. For this we developed a new method, which is based on Triangular Irregular Networks (TINs). For each void, we extracted points around the edge of the void from the SRTM DEM and the fill surface. TINs were calculated from these points and converted to a base surface for each dataset. The fill base surface was subtracted from the fill surface, and the result added to the SRTM base surface. The fill surface could then seamlessly be merged with the SRTM DEM. For validation, we compared the resulting DEM to the original SRTM surface, to the fill DEM and to a surface calculated by the International Center for Tropical Agriculture (CIAT) from the SRTM data. We calculated the differences between measured GPS positions and the respective surfaces for 187,500 points throughout the mountain range (∆GPS). Comparison of the means and standard deviations of these values showed that for the void areas, the fill surface was most accurate, with a standard deviation of the ∆GPS from the mean ∆GPS of 69 m, and only little accuracy was lost by merging it to the SRTM surface (standard deviation of 76 m). The CIAT model was much less accurate in these areas (standard deviation of 128 m). The results show that our method is capable of transferring the relative vertical accuracy of a fill surface to the void areas in the SRTM model, without introducing uncertainties about the absolute elevation of the fill surface. It is well suited for datasets with varying altitude biases, which is a common problem of older topographic information.

Little is known about the sustainability of irrigated oasis agriculture in northern Oman. The objective of this study therefore was to examine which factors allowed agricultural productivity to be apparently maintained during the two millenia of a mountain oasis’ existence. Soil moisture and physico-chemical properties were measured in a typical flood-irrigated field sown to alfalfa (Medicago sativa L.). Particle size, organic (Corg) and inorganic carbon content, pH and electrical conductivity (EC) of the soil profile were analyzed at 0.15, 0.45 and 1.00 m. Saturated hydraulic conductivity and the soil’s apparent bulk density and water potential were determined from undisturbed samples at 0.05, 0.25 and 0.60 m. During irrigation cycles of 6–9 days, volumetric water contents ranged from 30% to 13%. A tracer experiment with potassium bromide revealed that 52–56% of the irrigation water was stored in the upper 0.4 m of the soil. The rest of the water moved further down the profile, thus providing the necessary drainage to avoid the build-up of toxic salt concentrations. Due to differences in pore size, plant-available water in the topsoil amounted to 18.7% compared to 13% and 13.5% at 0.25- and 0.60-m depth, respectively. The aggregate structure in the upper 1.0 m of the profile is likely preserved by concentrations of calcium carbonate (CaCO3) from 379 to 434 mg kg � 1 and Corg from 157 to 368 mg kg � 1 soil. The data indicate that the sustainability of this irrigated landuse system is due to high water quality with low sodium but high CaCO3 concentration, the elaborate terrace structure and water management which allows adequate drainage. D 2004 Elsevier B.V. All rights reserved.

For millennia oasis agriculture has been the backbone of rural livelihood in the desertic Sultanate of Oman. However, little is known about the functioning of these oasis systems, in particular with respect to the C turnover. The objective was to determine the effects of crop, i.e. alfalfa, wheat and bare fallow on the CO 2 evolution rate during an irrigation cycle in relation to changes in soil water content and soil temperature. The gravimetric soil water content decreased from initially 24% to approximately 16% within 7 days after irrigation. The mean CO2 evolution rates increased significantly in the order fallow (27.4 mg C m −2 h −1 ) < wheat (45.5 mg C m −2 h −1 ) < alfalfa (97.5 mg C m −2 h −1 ). It can be calculated from these data that the CO2 evolution rate of the alfalfa root system was nearly four times higher than the corresponding rate in the wheat root system. The decline in CO2 evolution rate, especially during the first 4 days after irrigation, was significantly related to the decline in the gravimetric water content, with r = 0.70. CO2 evolution rate and soil temperature at 5 cm depth were negatively correlated (r =− 0.56, n = 261) due to increasing soil temperature with decreasing gravimetric water content. © 2003 Elsevier B.V. All rights reserved.