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Decision making in pest management is a challenging task. While pest dynamics are often quite uncertain, such decisions are often based on tenuous assumptions of certainty (economic injury levels and marginal utility approximations). To overcome such assumptions and adequately consider uncertainty, we apply decision analysis to evaluate management strategies used by farmers in the Colombian Caribbean against the boll weevil (BW). We represent the decision to protect the crop using partial budget analysis. This allows us to capture key properties of BW control strategies, while accounting for uncertainty about pest infestation pressure, control effectiveness and cotton yield and price. Our results indicate that proactive pest management is more efficient than reactive control given the current BW infestation pressure. However, farmers may prefer the reactive strategy, since they have experienced seasons with low infestation pressure where no insecticide applications were required. The proactive strategy, in contrast, requires scheduled pesticide applications in all years. Results show that in seasons with high infestation pressure the expected revenues of the reactive strategy tend to decrease, mainly because more spray applications are required when fields are heavily infested by the weevil. Value of information analysis revealed that uncertainties related to the start of the infestation, loss damage rate and attainable yield have the greatest influence on the decision recommendation for crop protection. Narrowing these key knowledge gaps may offer additional clarity on the performance of the current management strategies and provide guidance for the development of strategies to reduce insecticide use. This is particularly important for the promotion of the proactive strategy, which, under the current infestation pressure, has potential to reduce insecticide use. While economic injury levels can only be applied to responsive measures, our approach of partial budget analysis under uncertainty allows us to assess and compare both responsive and preventive measures in the same methodological framework. This framework can be extended to non-pesticide control measures.

Intervening into agricultural systems necessarily includes risks, uncertainties, and ultimately unknown outcomes. Decision analysis embraces uncertainty through an interdisciplinary approach that involves relevant stakeholders in evaluating complex decisions. We applied decision analysis approaches to prioritize 21 farm management interventions, which could be considered in certification schemes for banana production. We estimated their contribution to climate change adaptation and mitigation as well as ecological outcomes. We used a general model that estimated the impacts of each intervention on adaptation (benefits minus costs), mitigation (global warming potential), ecological parameters (e.g., biodiversity and water and soil quality), and farming aspects (e.g., yield, implementation costs and production risks). We used expert and documented knowledge and presented uncertainties in the form of 90% confidence intervals to feed the model and forecast the changes in system outcomes caused by each intervention compared to a baseline scenario without the measure. By iterating the model function 10,000 times, we obtained probability distributions for each of the outcomes and farm management interventions. Our results suggest that interventions associated with nutrient management (e.g., composting and nutrient management plan) positively affect climate change adaptation, mitigation, and ecological aspects. Measures with no direct yield benefits (e.g., plastic reduction) correlate negatively with adaptation but have positive impacts on ecology. Creating buffer zones and converting low-productivity farmland (incl. unused land) also have positive ecological and adaptation outcomes. Decision analysis can help in prioritizing farm management interventions, which may vary considerably in their relationship with the expected outcomes. Additional work may be required to elaborate a comprehensive assessment of the underlying aspects modulating the impacts of a given measure on the evaluated outcome. Our analysis provides insights on the most promising interventions for banana plantations and may help practitioners and researchers in focusing further studies.The online version contains supplementary material available at 10.1007/s13593-022-00809-0.

© 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.

The boll weevil [BW; Anthonomus grandis grandis Boheman (Coleoptera: Curculionidae)] is the main pest in the cotton-producing regions throughout the Americas from Southern Texas to Argentina. In the Colombian Caribbean, frequent population outbreaks have resulted in cotton planting bans in some localities and in massive applications of insecticides elsewhere (up to 15 insecticide sprays per cotton season). To date, information on boll weevil management strategies in Colombia is only available in the forms of gray literature (technical reports) and informal knowledge held by crop advisors and farmers. This study compiles this information using a standardized protocol for participatory construction of conceptual models for agricultural systems. The conceptual model developed in this study integrates the informal local knowledge of crop advisors and farmers with disciplinary knowledge describing management strategies for boll weevil. The collected data were assessed and organized using a systems approach to facilitate the future development of quantitative models and allow visualization of knowledge gaps. The model includes the description of the biological and technical-decisional subsystems. The latter subsystem explains boll weevil management at two temporal-spatial scales: 1) farm (field) scale management during the cotton season and 2) a regional boll weevil suppression strategy mainly aimed at controlling the insect populations that survived the intercotton season. The development of this conceptual model allowed describing the current management strategies for boll weevil and formulating hypotheses about the effectiveness of these strategies. This conceptual model provides guidelines for future research, and it can serve as a baseline for the development of quantitative models and simulations describing the decision-making process related to the management of boll weevil in the Colombian Caribbean.

Global warming has modified the phenology of deciduous species. Temperature during the dormancy phase modulates the timing of bloom in temperate trees. Chill and heat requirements represent the climatic needs of trees during dormancy. Partial Least Squares (PLS) regression allows delineating chilling and forcing phases, which in turn allows estimating the trees’ requirements. However, PLS regression requires long-term phenology and weather data, which are scarce in many growing regions. In a two-year experiment, we generated long-term phenology data by exposing potted trees to distinct environments during winter. We obtained records for 66 and 32 experimental seasons in apple and pear, respectively. We recorded tree phenology and hourly temperature. Through PLS regression methods, we determined the impacts of inter-seasonal variation on the outputs, estimated species’ dormancy phases and needs (in Chill Portions – CP and Growing Degree Hours – GDH), and assessed the relationship between bloom and temperature during the chilling and forcing phases. Results suggest inter-seasonal variation may be more important than number of seasons for producing valuable outputs. We delineated the chilling phase from October 19 to January 04 for apple and October 19 to December 27 for pear. The forcing period for both species was January 16 – March 26. Median chill and heat requirements were estimated as 43 CP and 14,845 GDH for apple and 31 CP and 11,816 GDH for pear. Bloom was modulated by temperature during both phases under warm conditions. In cold scenarios, bloom was mostly defined by temperatures during the forcing phase. We expanded the reach of the PLS regression method and made it applicable for cultivars lacking long-term phenology data. Our approach helps dormancy researchers improve their procedures to analyze species’ responses under possible future conditions. This work may assist farmers and orchard managers in adapting their orchards to face future challenges.

Sustainable land management is at the heart of some of the most intractable challenges facing humanity in the 21st century. It is critical for tackling biodiversity loss, land degradation, climate change and the decline of ecosystem services. It underpins food production, livelihoods, dietary health, social equity, climate change adaptation, and many other outcomes. However, interdependencies, trade-offs, time lags, and non-linear responses make it difficult to predict the combined effects of land management decisions. Policy decisions also have to be made in the context of conflicting interests, values and power dynamics of those living on the land and those affected by the consequences of land use decisions. This makes designing and coordinating effective land management policies and programmes highly challenging. The difficulty is exacerbated by the scarcity of reliable data on the impacts of land management on the environment and livelihoods. This poses a challenge for policymakers and practitioners in governments, development banks, non-governmental organisations, and other institutions. It also sets demands for researchers, who are under ever increasing pressure from funders to demonstrate uptake and impact of their work. Relatively few research methods exist that can address such questions in a holistic way. Decision makers and researchers need to work together to help untangle, contextualise and interpret fragmented evidence through systems approaches to make decisions in spite of uncertainty. Individuals and institutions acting as knowledge brokers can support these interactions by facilitating the co-creation and use of scientific and other knowledge. Given the patchy nature of data and evidence, particularly in developing countries, it is important to draw on the full range of available models, tools and evidence. In this paper we review the use of evidence to inform multiple-objective integrated landscape management policies and programmes, focusing on how to simultaneously achieve different sustainable development objectives in diverse landscapes. We set out key success factors for evidence-based decision-making, which are summarised into 10 key principles for integrated landscape management knowledge brokering in integrated landscape management and 12 key skills for knowledge brokers. We finally propose a decision-support framework to organise evidence that can be used to tackle different types of land management policy decision.

Agroforestry interventions have the potential to benefit the livelihoods of farmers and communities worldwide. However, given the high system complexity, the long-term benefits of agroforestry are difficult to anticipate. This study aimed to integrate uncertainty into long-term performance projections for agroforestry interventions in the highlands of Northwest Vietnam. We applied decision analysis and probabilistic modeling approaches to produce economic ex-ante assessments for seven agroforestry options (intercropping of maize, forage grass, or coffee with tea, nut, fruit, and timber trees) promoted in the region. Our results indicate that farmers likely prefer annual monocultures due to the relatively early incomes and short time-lag on returns. However, the results also show that annual profits from monocrops can be expected to decrease over time, due mainly to unsustainable soil use. Agroforestry systems, on the other hand, return substantial profits in the long term, but they also incur high establishment and maintenance costs and can generate net losses in the first few years. Initial financial incentives to compensate for these losses may help in promoting agroforestry adoption in the region. Uncertainties related to farmers’ time preference, crop yields, and crop prices appeared to have the greatest influence on whether monocropping or agroforestry emerged as the preferable option. Narrowing these key knowledge gaps may offer additional clarity on farmers’ optimal course of action and provide guidance for agencies promoting agroforestry interventions in Vietnam and elsewhere. Our model produced a set of plausible ranges for net present values and highlighted critical variables, more clarity on which would support decision-making under uncertainty. Our innovative research approach proved effective in providing forecasts of uncertain outcomes and can be useful for informing similar development interventions in other contexts.

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.

Homegardens are traditional food systems that have been adapted over generations to fit local cultural and ecological conditions. They provide a year-round diversity of nutritious foods for smallholder farming communities in many regions of the tropics and subtropics. In southwestern Uganda, homegardens are the primary source of food, providing a diverse diet for rural marginalized poor. However, national agricultural development plans as well as economic and social pressures threaten the functioning of these homegardens. The implications of these threats are difficult to evaluate, because the structure and functions of the homegardens are not well understood. The aim of the study was to identify patterns and influencing factors in the diversity of homegardens by documenting the floristic diversity and its interactions with spatial, environmental and socio-economic factors. A geographically and socially focused assessment of floristic diversity in 102 randomly selected homegardens in three districts of southwest Uganda was conducted along a deforestation gradient following a human ecology conceptual framework and testing multiple quantitative hypotheses regarding the above mentioned factors. A merged mixed-method approach was followed to provide context and feedback regarding quantitative findings. Results show a high total richness of 209 (mean 26.8 per homegarden) crop species (excluding weeds and ornamentals) dominated by food species, which constituted 96 percent of individuals and 44 percent of all species. Forest-edge homegardens maintained higher plant diversity compared to homegardens in deforested areas and near degraded wetlands. Multiple linear regression models indicated elevation, location, homegarden size, distance to market, additional land ownership (outside the homegarden) and livestock ownership as significant predictors of crop diversity. Cluster analysis of species densities revealed four garden types: ‘diverse tree gardens’, ‘small forest-edge gardens’, ‘large, old, species-rich gardens’, and ‘large, annual-dominated herb gardens’, with 98% correct classification. Location, elevation, and garden size were also important determinants in the cluster assignment. We conclude that the diversity of the studied homegardens may be changing as part of adaptive traditional practices and in response to external drivers. The identified patterns illustrate the importance of homegardens for rural livelihoods and may offer some ways to support farmers to maintain these systems as relevant mechanisms for development in Uganda.

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.

Agricultural landscapes are increasingly being managed with the aim of enhancing the provisioning of multiple ecosystem services and sustainability of production systems. However, agricultural management that maximizes provisioning ecosystem services can often reduce both regulating and maintenance services. We hypothesized that agroforestry reduces trade-offs between provisioning and regulating/maintenance services. We conducted a quantitative synthesis of studies carried out in sub-Saharan Africa focusing on crop yield (as an indicator of provisioning services), soil fertility, erosion control, and water regulation (as indicators of regulating/maintenance services). A total of 1106 observations were extracted from 126 peer-reviewed publications that fulfilled the selection criteria for meta-analysis of studies comparing agroforestry and non-agroforestry practices (hereafter control) in sub-Saharan Africa. Across ecological conditions, agroforestry significantly increased crop yield, total soil nitrogen, soil organic carbon, and available phosphorus compared to the control. Agroforestry practices also reduced runoff and soil loss and improved infiltration rates and soil moisture content. No significant differences were detected between the different ecological conditions, management regimes, and types of woody perennials for any of the ecosystem services. Main trade-offs included low available phosphorus and low soil moisture against higher crop yield. This is the first meta-analysis that shows that, on average, agroforestry systems in sub-Saharan Africa increase crop yield while maintaining delivery of regulating/maintenance ecosystem services. We also demonstrate how woody perennials have been managed in agricultural landscapes to provide multiple ecosystem services without sacrificing crop productivity. This is important in rural livelihoods where the range of ecosystem services conveys benefits in terms of food security and resilience to environmental shocks.

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.

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.

Wild edible fruits hold great potential for improving human diets, especially in agricultural societies of the developing world. In Africa, a well-known supplier of such fruits is the baobab (Adansonia digitata L., Malvaceae), one of the most remarkable trees of the world. Several studies in different African countries have highlighted this indigenous fruit tree as a priority species for domestication and expanded use. However, internationally available information on baobab in East Africa, particularly in Sudan and Kenya, remains scarce. This review aims to shed light on the ecology, diversity and current level of utilization of baobab in East Africa in order to facilitate domestication and conservation of the species. A list of priority research areas is provided at the end of the review to encourage further studies and investment in this unique plant taxon.

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.

Climate change may result in increasingly frequent extreme events, such as the unusually dry conditions that occurred in Germany during the apple growing season of 2018. To assess the effects of this phenomenon on dormancy release and flowering in apples, we compared irrigated and non-irrigated orchard blocks at Campus Klein-Altendorf. We evaluated bud development, dormancy release and flowering in the following season under orchard and controlled forcing conditions. Results showed that irrigated trees presented longer (39.2%) and thinner shoots compared to non-irrigated trees. In both treatments, apical buds developed a similar number of flower primordia per cyme (4&ndash, 5), presenting comparable development and starch dynamics during dormancy. Interestingly, buds on non-irrigated shoots exposed to low chill levels responded earlier to forcing conditions than those on irrigated shoots. However, chill requirements (~50 Chill Portions) and bud phenology under field conditions did not differ between treatments. In spring, buds on non-irrigated trees presented a higher bloom probability (0.42) than buds on irrigated trees (0.30). Our findings show that mild water stress during summer influenced vegetative growth during the same season, as well as the response of buds to forcing temperatures and flowering of the following season. The differences between irrigation levels in the phenological responses of shoots under low-chill conditions point to a so-far understudied impact of water supply on chilling requirements, as well as subsequent bud behavior. Accounting for the effects of both the water status during summer and the temperature during the dormant season may be required for accurately predicting future tree phenology in a changing climate.

The perennial life strategy of temperate trees relies on establishing a dormant stage during winter to survive unfavorable conditions. To overcome this dormant stage, trees require cool (i.e., chilling) temperatures as an environmental cue. Numerous approaches have tried to decipher the physiology of dormancy, but these efforts have usually remained relatively narrowly focused on particular regulatory or metabolic processes, recently integrated and linked by transcriptomic studies. This work aimed to synthesize existing knowledge on dormancy into a general conceptual framework to enhance dormancy comprehension. The proposed conceptual framework covers four physiological processes involved in dormancy progression: (i) transport at both whole-plant and cellular level, (ii) phytohormone dynamics, (iii) genetic and epigenetic regulation, and (iv) dynamics of nonstructural carbohydrates. We merged the regulatory levels into a seasonal framework integrating the environmental signals (i.e., temperature and photoperiod) that trigger each dormancy phase.

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.

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.

Woody perennial species from temperate regions fall dormant during the cold winter season to avoid unfavourable conditions. To break out of dormancy and eventually flower, they must fulfil cultivar-specific chilling and heat requirements. Phenology analysis can clarify the climatic requirements of tree cultivars and thus provide critical information to ensure the future viability of orchards in warm growing regions, where warmer winters are expected as a result of climate change. We used Partial Least Squares (PLS) regression to correlate first bloom dates of 4 local and 3 foreign pistachio ( Pistacia vera L.) cultivars with daily chill and heat accumulation (quantified with the Dynamic Model and Growing Degree Hours Model, respectively) for 18-year records (1997–2016) from Sfax, Tunisia. PLS outputs allowed delineation of the chilling phase, during which high chill accumulation was correlated to early bloom, and the forcing phase, when this was true for high heat accumulation. Both phases showed discontinuities. During September and October, high heat accumulation appeared to first have a bloom-delaying effect, followed by a bloom-advancing effect, indicating that temperature during dormancy induction may affect bloom dates. Chilling requirements were estimated between 32.1 ± 2.3 and 33.3 ± 2.2 Chill Portions and heat requirements between 9974 ± 198 and 12,738 ± 235 Growing Degree Hours. This study revealed limitations of the Dynamic Model, which is often considered the most accurate among commonly used models, in the warm Tunisian climate. High temperatures during the chilling phase had a significant bloom-delaying effect on all pistachio cultivars. Low chill accumulation was related to very low yields and associated with zero production in 1995, 2001 and 2007. Low flowering percentage, high bud fall percentage, long and inhomogeneous bloom, and co-occurrence of several phenological stages on the same branch were symptoms of lack of chill in 2016.

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.

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 isolated Sudanese baobab (Adansonia digitata L.) populations, located at the northernmost limit of the East African distributional range of the species, are regarded as important genetic resources. The morphological variation in fruits of selected baobab trees in Kordofan, Sudan, was evaluated by sampling fruits and assessing their characteristics. Furthermore, locations and stem diameter at breast height of 240 baobabs were mapped for a stand in Kordofan. Our preliminary results indicated a high diversity in fruit phenotypes. Ventricose, crescent-shaped, globose and fusiform fruit types were identified. Fruit shape varied between trees but was consistent within each individual tree. Percentage of fruit pulp varied between the different fruit types with 14, 15, 18, and 21 % recorded for ventricose, fusiform, crescent-shaped and globose fruits, respectively. Interesting was also the observation of baobab morphotypes that retained leaves during the dry season. Variation in leaf morphology could also be recognised. Measurements of baobab trees revealed a density of 0.72 individuals ha−1. Stem diameters ranged from 0.06 to 4.77 m. The size class distribution (SCD) showed an inverse J-shaped curve with a SCD slope of −0.57 which indicates a viable regenerating population. Based on the results recorded, enhancement of scientific research activities on the almost unstudied baobabs in Kordofan, Sudan is highly recommended.

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.

Several botanical studies have been conducted in different parts of Oman, but knowledge about agro-biodiversity in the rapidly decaying ancient mountain oases of this country remains scarce. To fill this gap we assessed the genetic resources of three mountain oases in the al-Hajar range using a GIS-based field survey and farmer interviews. While arid conditions prevail throughout the mountain range, the different elevations of Balad Seet (950–1020 m a.s.l.), Maqta (930–1180 m a.s.l.) and Al Jabal al Akhdar (1750–1930 m a.s.l.) provide markedly differing agro-climatic conditions. Overall, 107 different crop species were identified belonging to 39 families. Species number was highest among fruits (33 spp.), followed by vegetables (24 spp.). Intensive irrigation allows cultivation of a broad range of species at all oases. However, the number of species varied significantly between sites. Fruit species diversity and homogeneity of distribution of individual fruit species was highest at Balad Seet and lowest at Maqta as indicated by respective Shannon indices of 1.00 and 0.39 and evenness values of 32% and 16%. Century plant (Agave americana L.), faba bean (Vicia faba L. var. minor Peterm. em. Harz) and lentil (Lens culinaris Medik.) were identified as relict crops, supporting oral reports of past cultivation and providing evidence of genetic erosion. Some species, such as the temperate fruits of Al Jabal al Akhdar, were exclusively found at the coolest site, while others only occurred at the hotter locations. Overall greatest species similarity was found between Balad Seet and Al Jabal al Akhdar as indicated by a Sorensen coefficient of similarity of 67%. At all oases a multilayered vegetation structure dominated with a canopy, an understory and a ground layer. Greatest species richness was recorded in the lowest stratum. Overall the study shows a location-specific but surprisingly diverse mosaic of crops in Omani mountain oases which merits further studies and conservation efforts.