Using experience with bambara groundnut (Vigna subterranea), this paper examines how local knowledge, genetic evaluation, research in fields, glasshouses and laboratories, and crop simulation modelling might be linked within a methodological framework to assess rapidly the potential of any underutilized crop. The approach described is retrospective in that each activity was not clearly defined and structured at the outset. However, the experience gained may help to establish a methodology by which growers, researchers and international agencies can integrate their knowledge and understanding of any particular underutilized crop and apply similar principles to accelerate the acquisition of knowledge on other underutilized species. The use of a methodological framework provides a basis for activities that maximize knowledge, minimize duplication of effort, identify priority areas for further research and dissemination, and derive general principles for application across underutilized crops in general. It also allows policy makers and planners to make comparative decisions on the nutritional, economic and research importance of different underutilized and more-favoured species. In particular, the incorporation of a generic crop simulation model within the methodological framework may assist growers, extension agencies and scientists to refine general recommendations for any particular crop to local conditions. Also, the incorporation of information gathered from the field, laboratory or market can be used to update rapidly the predictive capacity of the model for each crop.

In many cases, quantitative information on production can only be obtained through crop simulation studies Mechanistic crop growth models have many potential uses for crop and long-term climatic records (MacDonald and Hall, management. These models can aid in preseason and within-season management decisions for cultural practices such as fertilizer and 1980;Matis et al., 1985; Bouman et al., 1995). Underirrigation applications and pest and disease management. When mak-standing the impacts of weather on crop production by ing these management decisions, maximizing yield and net return as applying simulation models provides a credible basis for a function of inputs and production costs is one of the fundamental a quantitative estimate of the range of yields farmers goals. Reliable yield forecasting within the growing season would can expect for a given set of management conditions enable improved planning and more efficient management of grain (Arkin and Dugas, 1981, Hammer et al., 1996; Tsuji et production, handling, and marketing. The objective of this study was al., 1998). to determine if the dynamic simulation model CERES-Wheat could The use of crop simulation models for predicting crop be used to forecast final grain yield and crop biomass within the yield as function of weather and climate has been studied growing season for environmental and management conditions in the extensively (Hoogenboom, 2000). These applications United Kingdom (UK). Experimental data for three seasons and four sites were used for model calibration and evaluation. A stochastic range from predicting yield at a farm level to predicting approach was applied, based on multiple years of weather data gener-regional and national yield levels although large-scale ated with the weather generator SIMMETEO. Yield forecasts were predictions are normally more common (Travasso and conducted for five different developmental stages within the growing Delecolle, 1995; Supit, 1997). Most of these prediction season. For each forecast date, observed weather data were used up applications include forecasts that are conducted before to the forecast date and supplemented with generated weather data planting while some simulations are conducted during until final harvest was predicted. Eighty-nine different sequences of the growing season. The improved understanding of El generated weather data were used for each forecast. Predicted grain Nino and the Southern Oscillation phenomenon has yield had a root mean square difference (RMSD) ranging from 0.95 especially led to many applications that are based on t ha Ϫ1 for the first forecast date to 0.68 t ha Ϫ1 for the final forecast seasonal climate forecasts (Hammer et al., 1996; Meinke date while the RMSD for total predicted biomass ranged from 3.59 to 2.09 t ha Ϫ1 . An analysis of predicted final grain yield and biomass tailored for the specific needs of the farming community

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Meteorological drought is a natural hazard that can occur under all climatic regimes. Monitoring the drought is a vital and important part of predicting and analyzing drought impacts. Because no single index can represent all facets of meteorological drought, we took a multi-index approach for drought monitoring in this study. We assessed the ability of eight precipitation-based drought indices (SPI (Standardized Precipitation Index), PNI (Percent of Normal Index), DI (Deciles index), EDI (Effective drought index), CZI (China-Z index), MCZI (Modified CZI), RAI (Rainfall Anomaly Index), and ZSI (Z-score Index)) calculated from the station-observed precipitation data and the AgMERRA gridded precipitation data to assess historical drought events during the period 1987-2010 for the Kashafrood Basin of Iran. We also presented the Degree of Dryness Index (DDI) for comparing the intensities of different drought categories in each year of the study period (1987-2010). In general, the correlations among drought indices calculated from the AgMERRA precipitation data were higher than those derived from the station-observed precipitation data. All indices indicated the most severe droughts for the study period occurred in 2001 and 2008. Regardless of data input source, SPI, PNI, and DI were highly inter-correlated (R 2 =0.99). Furthermore, the higher correlations (R 2 =0.99) were also found between CZI and MCZI, and between ZSI and RAI. All indices were able to track drought intensity, but EDI and RAI showed higher DDI values compared with the other indices. Based on the strong correlation among drought indices derived from the AgMERRA precipitation data and from the station-observed precipitation data, we suggest that the AgMERRA precipitation data can be accepted to fill the gaps existed in the station-observed precipitation data in future studies in Iran. In addition, if tested by station-observed precipitation data, the AgMERRA precipitation data may be used for the data-lacking areas.

In this study the sensitivity of peach tree (Prunus persica L.) to three water stress levels from mid-pit hardening until harvest was assessed. Seasonal patterns of shoot and fruit growth, gas exchange (leaf photosynthesis, stomatal conductance and transpiration) as well as carbon (C) storage/mobilization were evaluated in relation to plant water status. A simple C balance model was also developed to investigate sink-source relationship in relation to plant water status at the tree level. The C source was estimated through the leaf area dynamics and leaf photosynthesis rate along the season. The C sink was estimated for maintenance respiration and growth of shoots and fruits. Water stress significantly reduced gas exchange, and fruit, and shoot growth, but increased fruit dry matter concentration. Growth was more affected by water deficit than photosynthesis, and shoot growth was more sensitive to water deficit than fruit growth. Reduction of shoot growth was associated with a decrease of shoot elongation, emergence, and high shoot mortality. Water scarcity affected tree C assimilation due to two interacting factors: (i) reduction in leaf photosynthesis (-23% and -50% under moderate (MS) and severe (SS) water stress compared to low (LS) stress during growth season) and (ii) reduction in total leaf area (-57% and -79% under MS and SS compared to LS at harvest). Our field data analysis suggested a Ψstem threshold of -1.5 MPa below which daily net C gain became negative, i.e. C assimilation became lower than C needed for respiration and growth. Negative C balance under MS and SS associated with decline of trunk carbohydrate reserves – may have led to drought-induced vegetative mortality.

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Climate change projections predict warmer and drier conditions. In general, moderate to severe water stress reduce plant vegetative growth and leaf photosynthesis. However, vegetative and reproductive growths show different sensitivities to water deficit. In fruit trees, water restrictions may have serious implications not only on tree growth and yield, but also on fruit quality, which might be improved. Therefore, it is of paramount importance to understand the complex interrelations among the physiological processes involved in within-tree carbon acquisition and allocation, water uptake and transpiration, organ growth, and fruit composition when affected by water stress. This can be studied using process-based models of plant functioning, which allow assessing the sensitivity of various physiological processes to water deficit and their relative impact on vegetative growth and fruit quality. In the current study, an existing fruit-tree model (QualiTree) was adapted for describing the water stress effects on peach (Prunus persica L. Batsch) vegetative growth, fruit size and composition. First, an energy balance calculation at the fruit-bearing shoot level and a water transfer formalization within the plant were integrated into the model. Next, a reduction function of vegetative growth according to tree water status was added to QualiTree. Then, the model was parameterized and calibrated for a late-maturing peach cultivar (“Elberta”) under semi-arid conditions, and for three different irrigation practices. Simulated vegetative and fruit growth variability over time was consistent with observed data. Sugar concentrations in fruit flesh were well simulated. Finally, QualiTree allowed for determining the relative importance of photosynthesis and vegetative growth reduction on carbon acquisition, plant growth and fruit quality under water constrains. According to simulations, water deficit impacted vegetative growth first through a direct effect on its sink strength, and; secondly, through an indirect reducing effect on photosynthesis. Fruit composition was moderately affected by water stress. The enhancements performed in the model broadened its predictive capabilities and proved that QualiTree allows for a better understanding of the water stress effects on fruit-tree functioning and might be useful for designing innovative horticultural practices in a changing climate scenario.