Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment.

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.

Aphid Resistance: An Overlooked Ecological Dimension of Nonstructural Carbohydrates in Cereals.

Nonstructural carbohydrates in cereals have been widely investigated from physiological, genetic, and breeding perspectives. Nonstructural carbohydrates may contribute to grain filling, but correlations with yield are inconsistent and sometimes negative. Here we ask if there are hidden functions of nonstructural carbohydrates, advance an ecological dimension to this question, and speculate that high concentration of nonstructural carbohydrates may challenge the osmotic homeostasis of aphids, thus providing a working hypothesis that connects nonstructural carbohydrates with aphid resistance in cereals. In the light of this proposition, the amount and concentration of nonstructural carbohydrates should be regarded as functionally different traits, with amount relevant to the carbon economy of the crop and concentration playing an osmotic role. We conclude with suggestions for experiments to test our hypothesis.

Climate change impact and adaptation for wheat protein.

Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32-multi-model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low-rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2 . Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by -1.1 percentage points, representing a relative change of -8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production.

Multimodel ensembles improve predictions of crop-environment-management interactions.

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.

Author Correction: The uncertainty of crop yield projections is reduced by improved temperature response functions.

Nature Plants 3, 17102 (2017); published online 17 July 2017; corrected online 27 September 2017.

Erratum: The uncertainty of crop yield projections is reduced by improved temperature response functions.

This corrects the article DOI: 10.1038/nplants.2017.102.

Erratum: The uncertainty of crop yield projections is reduced by improved temperature response functions.

This corrects the article DOI: 10.1038/nplants.2017.102.

The uncertainty of crop yield projections is reduced by improved temperature response functions.

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.

Balancing crop yield and water productivity tradeoffs in herbaceous and woody crops.

The links between water and crop yield are well known. In agricultural systems, maximum yield and maximum water productivity (WP; yield divided by water use) are not always compatible goals. In water-limited situations, optimal solutions must be reached by finding a compromise between the levels of crop production and WP. The tradeoffs between production and WP are reviewed here and the dominant effects of the environment on WP are examined. Genetic improvement for WP generally has yield tradeoffs, whereas management measures devised to improve WP also enhance yield. It is shown that partial closure of the stomata in response to environmental stimuli has a variable impact on canopy transpiration, depending on the degree of coupling between the canopy and the atmosphere. In contrast to the behaviour of the major herbaceous crops, WP increases in some woody crops in response to water stress, suggesting that biomass and transpiration are not linearly related, and that deficit irrigation should be successful in these species. Avoiding high evaporative demand periods (e.g. through tolerance to low temperatures) is an important option that aims to increase production and WP. A case study is presented for improving sunflower (Helianthus annuus L.) yield and WP in temperate environments.

Reflections on food security under water scarcity.

Forecasts on population growth and economic development indicate that there will be substantial increases in food demand for the forthcoming decades. We focus here on the water requirements of food production, on the issue of whether there would be enough water to produce sufficient food in the future, and we offer options to face this challenge based on recent trends observed in some agricultural systems. Given the competition for water faced by the agricultural sector, and the uncertainties associated with climate change, improving the efficiency of water use in both rain-fed and irrigated systems is the main avenue to face the challenge. In rain-fed agriculture, managing the risk associated with rainfall variability is a promising option to increase productivity. In irrigated systems, a case study on the improvements in water productivity in Andalusia, Spain, is used to illustrate some of the opportunities to make progress. Progress in reducing irrigation water use in recent decades has been substantial, but decreasing the consumptive use of crops is a much more difficult challenge. The need for more research and technology transfer on improving water-limited crop production is highlighted, and emphasis is placed on interdisciplinary approaches to gain the insight needed to achieve new breakthroughs that would help in tackling this complex problem.

A model of daily mean canopy conductance for calculating transpiration of olive canopies.

We tested the hypothesis that the transpiration (λEp) of high-coupled canopies, such as olive groves, may be calculated on a daily basis with sufficient precision by the Penman-Monteith 'big leaf' equation, by a model of bulk daily canopy conductance (gc) capable of scaling for canopy dimension. Given the limited data required, such a model could replace the standard approach (ET0 × Kc) for calculating olive water requirements, enhancing the precision of estimates. We developed a specific model of daily gc for unstressed olive canopies that was calibrated by transpiration measurements obtained by water balance from a 2-year experiment in a mature orchard with λEp ranging from 0.6 (February 1993) to 11.5 (July 1994) MJ m-2 day-1 and where leaf area index (L) changed from 1.25 to 2.5. The model uses the intercepted fraction of daily PAR and a linear function of average daytime temperature. The model was validated with λEp data collected by eddy covariance in a 3-year experiment conducted in a growing orchard that differed in L and cultivar from the one used in the calibration. The gc model, when used in the Penman-Monteith equation, gave very good daily λEp predictions for all seasons during 3 years, ranging from 0.5 (November 1998) to 5.5 (June 2000) MJ m-2 day-1, indicating that the goals of dealing with the dependence of olive gc on L and of simulating the seasonal variations in gc were achieved. A comparison with the Jarvis gc model, calibrated with 2 months of measured gc hourly data, showed that the gc model developed here performed better than the Jarvis model for the 3-year dataset. The exception to this was the period in which the Jarvis model was calibrated. This indicates that (1) the Jarvis model did not account for the seasonal variations in gc of the olive trees; and (2) the spatial and temporal scale assumptions required in the calibration of gc generate seasonal errors in the simulated bulk daily λEp for this crop. The applicability of this bulk gc model is restricted to well watered olive canopies and to the one-layer approach of calculating λEp but it could be adapted to rain-fed canopies in the future.

Deficit irrigation for reducing agricultural water use.

At present and more so in the future, irrigated agriculture will take place under water scarcity. Insufficient water supply for irrigation will be the norm rather than the exception, and irrigation management will shift from emphasizing production per unit area towards maximizing the production per unit of water consumed, the water productivity. To cope with scarce supplies, deficit irrigation, defined as the application of water below full crop-water requirements (evapotranspiration), is an important tool to achieve the goal of reducing irrigation water use. While deficit irrigation is widely practised over millions of hectares for a number of reasons - from inadequate network design to excessive irrigation expansion relative to catchment supplies - it has not received sufficient attention in research. Its use in reducing water consumption for biomass production, and for irrigation of annual and perennial crops is reviewed here. There is potential for improving water productivity in many field crops and there is sufficient information for defining the best deficit irrigation strategy for many situations. One conclusion is that the level of irrigation supply under deficit irrigation should be relatively high in most cases, one that permits achieving 60-100% of full evapotranspiration. Several cases on the successful use of regulated deficit irrigation (RDI) in fruit trees and vines are reviewed, showing that RDI not only increases water productivity, but also farmers' profits. Research linking the physiological basis of these responses to the design of RDI strategies is likely to have a significant impact in increasing its adoption in water-limited areas.