Maize yield reduction and economic losses caused by ground-level ozone pollution with exposure- and flux-response relationships in the North China Plain.

Ground-level ozone (O3) has negative effects on agricultural crops. Maize is an important grain crop in China. The North China Plain (NCP) serves as the major crops' production area of China and experiences severe ozone pollution. Using the ground-level ozone simulated by an atmospheric chemistry transport model (WRF-Chem), we quantified the yield reduction and economic losses of maize during 2015-2018 over NCP based on exposure-response AOT40 (accumulation of hourly O3 concentration exceed 40 ppb) and flux-response POD6 (phytotoxic dose of ozone over 6 nmol m-2 s-1). Results showed that the ozone concentration, AOT40, and POD6 clearly increased from 2015 to 2018 in growing season of maize over NCP. The four-year annual mean ozone concentration, AOT40, and POD6 were 0.055 ppm, 18.02 ppm h, and 5.02 mmol m-2, respectively. At county level, the relative loss of maize yield (MRYL) based on AOT40 and POD6 had clearly spatio-temporal differences in NCP. The average MRYLs of AOT40 and of POD6 from 2015 to 2018 were 10.4% and 21.4%, respectively, and these reductions were associated with 2399 million and 5637 million US dollars, respectively. This study suggests that surface ozone increased the yield losses of maize, and indicates that further reductions in ozone concentrations can enhance the food security in China.

Harnessing root architecture to address global challenges.

Root architecture can be targeted in breeding programs to develop crops with better capture of water and nutrients. In rich nations, such crops would reduce production costs and environmental pollution and, in developing nations, they would improve food security and economic development. Crops with deeper roots would have better climate resilience while also sequestering atmospheric CO2 . Deeper rooting, which improves water and N capture, is facilitated by steeper root growth angles, fewer axial roots, reduced lateral branching, and anatomical phenotypes that reduce the metabolic cost of root tissue. Mechanical impedance, hypoxia, and Al toxicity are constraints to subsoil exploration. To improve topsoil foraging for P, K, and other shallow resources, shallower root growth angles, more axial roots, and greater lateral branching are beneficial, as are metabolically cheap roots. In high-input systems, parsimonious root phenotypes that focus on water capture may be advantageous. The growing prevalence of Conservation Agriculture is shifting the mechanical impedance characteristics of cultivated soils in ways that may favor plastic root phenotypes capable of exploiting low resistance pathways to the subsoil. Root ideotypes for many low-input systems would not be optimized for any one function, but would be resilient against an array of biotic and abiotic challenges. Root hairs, reduced metabolic cost, and developmental regulation of plasticity may be useful in all environments. The fitness landscape of integrated root phenotypes is large and complex, and hence will benefit from in silico tools. Understanding and harnessing root architecture for crop improvement is a transdisciplinary opportunity to address global challenges.

Disparities among crop species in the evolution of growth rates: the role of distinct origins and domestication histories.

Growth rates vary widely among plants with different strategies. For crops, evolution under predictable and high-resource environments might favour rapid resource acquisition and growth, but whether this strategy has consistently evolved during domestication and improvement remains unclear. Here we report a comprehensive study of the evolution of growth rates based on comparisons among wild, landrace, and improved accessions of 19 herbaceous crops grown under common conditions. We also examined the underlying growth components and the influence of crop origin and history on growth evolution. Domestication and improvement did not affect growth consistently, that is growth rates increased or decreased or remained unchanged in different crops. Crops selected for fruits increased the physiological component of growth (net assimilation rate), whereas leaf and seed crops showed larger domestication effects on morphology (leaf mass ratio and specific leaf area). Moreover, climate and phylogeny contributed to explaining the effects of domestication and changes in growth. Crop-specific responses to domestication and improvement suggest that selection for high yield has not consistently changed growth rates. The trade-offs between morpho-physiological traits and the distinct origins and histories of crops accounted for the variability in growth changes. These findings have far-reaching implications for our understanding of crop performance and adaptation.

Comprehensive Phytotoxicity Assessment Protocol for Engineered Nanomaterials.

In order for nanotechnology to be sustainably applied in agriculture, emphasis should be on comprehensive assessment of multiple endpoints, including biouptake and localization of engineered nanomaterials (ENMs), potential effects on food nutrient quality, oxidative stress responses, and crop yield, before ENMs are routinely applied in consumer and agronomic products. This chapter succinctly outlines a protocol for conducting nanophytotoxicity studies focusing on nanoparticle purification and characterization, arbuscular mycorrhizal fungi (AMF)/symbiont inoculation, biouptake and translocation/localization, varied endpoints of oxidative stress responses, and crop yield.

Maize yield in smallholder agriculture system-An approach integrating socio-economic and crop management factors.

Yield gaps of maize (Zea mays L.) in the smallholder farms of eastern India are outcomes of a complex interplay of climatic variations, soil fertility gradients, socio-economic factors, and differential management intensities. Several machine learning approaches were used in this study to investigate the relative influences of multiple biophysical, socio-economic, and crop management features in determining maize yield variability using several machine learning approaches. Soil fertility status was assessed in 180 farms and paired with the surveyed data on maize yield, socio-economic conditions, and agronomic management. The C&RT relative variable importance plot identified farm size, total labor, soil factors, seed rate, fertilizer, and organic manure as influential factors. Among the three approaches compared for classifying maize yield, the artificial neural network (ANN) yielded the least (25%) misclassification on validation samples. The random forest partial dependence plots revealed a positive association between farm size and maize productivity. Nonlinear support vector machine boundary analysis for the eight top important variables revealed complex interactions underpinning maize yield response. Notably, farm size and total labor synergistically increased maize yield. Future research integrating these algorithms with empirical crop growth models and crop simulation models for ex-ante yield estimations could result in further improvement.

Eco-evolutionary agriculture: Host-pathogen dynamics in crop rotations.

Since its origins, thousands of years ago, agriculture has been challenged by the presence of evolving plant pathogens. Temporal rotations of host and non-host crops have helped farmers to control epidemics among other utilities, but further efforts for strategy assessment are needed. Here, we present a methodology for developing crop rotation strategies optimal for control of pathogens informed by numerical simulations of eco-evolutionary dynamics in one field. This approach can integrate agronomic criteria used in crop rotations-soil quality and cash yield-and the analysis of pathogen evolution in systems where hosts are artificially selected. Our analysis shows which rotation patterns perform better in maximising crop yield when an unspecified infection occurs, with yield being dependent on both soil quality and the strength of the epidemic. Importantly, the use of non-host crops, which both improve soil quality and control the epidemic results in similar rational rotation strategies for diverse agronomic and infection conditions. We test the repeatability of the best rotation patterns over multiple decades, an essential end-user goal. Our results provide sustainable strategies for optimal resource investment for increased food production and lead to further insights into the minimisation of pesticide use in a society demanding ever more efficient agriculture.

Energy costs of salt tolerance in crop plants.

Agriculture is expanding into regions that are affected by salinity. This review considers the energetic costs of salinity tolerance in crop plants and provides a framework for a quantitative assessment of costs. Different sources of energy, and modifications of root system architecture that would maximize water vs ion uptake are addressed. Energy requirements for transport of salt (NaCl) to leaf vacuoles for osmotic adjustment could be small if there are no substantial leaks back across plasma membrane and tonoplast in root and leaf. The coupling ratio of the H+ -ATPase also is a critical component. One proposed leak, that of Na+ influx across the plasma membrane through certain aquaporin channels, might be coupled to water flow, thus conserving energy. For the tonoplast, control of two types of cation channels is required for energy efficiency. Transporters controlling the Na+ and Cl- concentrations in mitochondria and chloroplasts are largely unknown and could be a major energy cost. The complexity of the system will require a sophisticated modelling approach to identify critical transporters, apoplastic barriers and root structures. This modelling approach will inform experimentation and allow a quantitative assessment of the energy costs of NaCl tolerance to guide breeding and engineering of molecular components.

Energy costs of salinity tolerance in crop plants: night-time transpiration and growth.

Plants grow and transpire during the night. The aim of the present work was to assess the relative flows of carbon, water and solutes, and the energy involved, in sustaining night-time transpiration and leaf expansive growth under control and salt-stress conditions. Published and unpublished data were used, for barley plants grown in presence of 0.5-1 mM NaCl (control) and 100 mM NaCl. Night-time leaf growth presents a more efficient use of taken-up water compared with day-time growth. This efficiency increases several-fold with salt stress. Night-time transpiration cannot be supported entirely through osmotically driven uptake of water through roots under salt stress. Using a simple three- (root medium/cytosol/vacuole) compartment approach, the energy required to support cell expansion during the night is in the lower percentage region (0.03-5.5%) of the energy available through respiration, under both, control and salt-stress conditions. Use of organic (e.g. hexose equivalents) rather than inorganic (e.g. Na+ , Cl- , K+ ) solutes for generation of osmotic pressure in growing cells, increases the energy demand by orders of magnitude, yet requires only a small portion of carbon assimilated during the day. Night-time transpiration and leaf expansive growth should be considered as a potential acclimation mechanism to salinity.

Geographic Object-Based Analysis of Airborne Multispectral Images for Health Assessment of Capsicum annuum L. Crops.

Vegetation health assessment by using airborne multispectral images throughout crop production cycles, among other precision agriculture technologies, is an important tool for modern agriculture practices. However, to really take advantage of crop fields imagery, specialized analysis techniques are needed. In this paper we present a geographic object-based image analysis (GEOBIA) approach to examine a set of very high resolution (VHR) multispectral images obtained by the use of small unmanned aerial vehicles (UAVs), to evaluate plant health states and to generate cropland maps for Capsicum annuum L. The scheme described here integrates machine learning methods with semi-automated training and validation, which allowed us to develop an algorithmic sequence for the evaluation of plant health conditions at individual sowing point clusters over an entire parcel. The features selected at the classification stages are based on phenotypic traits of plants with different health levels. Determination of areas without data dependencies for the algorithms employed allowed us to execute some of the calculations as parallel processes. Comparison with the standard normalized difference vegetation index (NDVI) and biological analyses were also performed. The classification obtained showed a precision level of about 95 % in discerning between vegetation and non-vegetation objects, and clustering efficiency ranging from 79 % to 89 % for the evaluation of different vegetation health categories, which makes our approach suitable for being incorporated at C. annuum crop's production systems, as well as to other similar crops. This methodology can be reproduced and adjusted as an on-the-go solution to get a georeferenced plant health estimation.

Legumes-The art and science of environmentally sustainable agriculture.

Symbiotic nitrogen fixation, which is carried out by the legume-rhizobia partnership, is a major source of nitrogen acquisition in natural ecosystems and in agriculture. The benefits to the plant gained through the rhizobial-legume symbiosis can be further enhanced by associations of the legume with arbuscular mycorrhiza. The progressive engagement of the legume host with the rhizobial bacteria and mycorrhizal fungi requires an extensive exchange of signalling molecules. These signals alter the transcriptional profiles of the partners, guiding and enabling extensive microbial and fungal proliferation in the roots. Such interactions and associations are greatly influenced by environmental stresses, which also severely limit the productivity of legume crops. Part II of the Special Issue on Legumes provides new insights into the mechanisms that underpin sustainable symbiotic partnerships, as well as the effects of abiotic stresses, such as drought, waterlogging, and salinity on legume biology. The requirement for germplasm and new breeding methods is discussed as well as the future of legume production in the face of climate change.

Effects of temperature, precipitation and carbon dioxide concentrations on the requirements for crop irrigation water in China under future climate scenarios.

Maize, rice, wheat and soybean-the major staple food crops in China-have a crucial role in national food security and economic development. Predictions of changes in the requirements for irrigation water in food crop production under climate change may provide scientific support for the optimum allocation of water resources and measures to mitigate climate change. We conducted a spatial grid-based analysis using projections of future climate generated by a bias-correction and spatial disaggregation multi-model ensemble for three representative concentration pathway scenarios (RCP2.6, RCP4.5 and RCP8.5) adopted by the fifth phase of the Coupled Model Intercomparison Project. We investigated the effects of climate change associated with increasing temperature, changed precipitation and increased concentrations of atmospheric carbon dioxide (CO2) on the irrigation water requirements of maize, rice, wheat and soybean in China at the end of the 21st century (2081-2100). Our results indicate that the irrigation water requirements of maize and wheat are driven by temperature and especially by CO2 concentrations in the northwest interior area as a result of the low rainfall and high rates of evaporation; the irrigation water requirement of soybean is influenced by a combined effect of temperature, precipitation and CO2 concentration, whereas the irrigation water requirement for rice is dominated by precipitation alone in the southern coastal region, which has high rainfall. The irrigation water requirements of crops decrease mainly as a result of the beneficial effects of CO2 on plant growth in China. The regions requiring vast amounts of irrigation water as a result of climate change are mainly concentrated in northwestern China. The effects of climate change affect the requirement for irrigation water, especially under high-emission scenarios, and should be studied further to design appropriate adaptation strategies for the management of agricultural water to maintain the sustainable development of agriculture.

Biogas plants as key units of biorefinery concepts: Options and their assessment.

In order to minimize the environmental impacts of growing population, progressive exploitation of fossil resources and negative consequences of climate change the politically intended goal is to successively transform our primarily oil-based into a bio-based economy. Hence, one goal is to significantly reduce the utilization of fossil resources by increasing the use of renewable energy and resources (i.e. biomass) and the efficiency of their conversion processes. Including existing technologies into the development of future concepts could accelerate the transition to a bio-economy. As one solution integrated biorefinery concepts based on agricultural biogas plants are discussed, which convert biomass with minimal energy consumption to a multitude of products without generating waste. However, they still have huge potential in terms of increased biomass utilization. In that context, catch crops offer interesting opportunities as a substrate for those biorefineries, since they support soil regeneration while generating additional products for the bio-economy without increasing land use. In this study a selection of significant indicators was chosen in order to determine the environmental effectivity and economic efficiency of these biorefinery concepts by a systematic assessment of possible process schemes. Thus within this study the usability of the chosen indicators and the potential of catch crops in advanced biorefineries is assessed.

Where are the drought tolerant crops? An assessment of more than two decades of plant biotechnology effort in crop improvement.

Since the dawn of modern biotechnology public and private enterprise have pursued the development of a new breed of drought tolerant crop products. After more than 20 years of research and investment only a few such products have reached the market. This is due to several technical and market constraints. The technical challenges include the difficulty in defining tractable single-gene trait development strategies, the logistics of moving traits from initial to commercial genetic backgrounds, and the disconnect between conditions in farmer's fields and controlled environments. Market constraints include the significant difficulty, and associated costs, in obtaining access to markets around the world. Advances in the biology of plant water management, including response to water deficit reveal new opportunities to improve crop response to water deficit and new genome-based tools promise to usher in the next era of crop improvement. As biotechnology looks to improve crop productivity under drought conditions, the environmental and food security advantages will influence public perception and shift the debate toward benefits rather than risks.

Integrating Plant Science and Crop Modeling: Assessment of the Impact of Climate Change on Soybean and Maize Production.

Increasing global CO2 emissions have profound consequences for plant biology, not least because of direct influences on carbon gain. However, much remains uncertain regarding how our major crops will respond to a future high CO2 world. Crop model inter-comparison studies have identified large uncertainties and biases associated with climate change. The need to quantify uncertainty has drawn the fields of plant molecular physiology, crop breeding and biology, and climate change modeling closer together. Comparing data from different models that have been used to assess the potential climate change impacts on soybean and maize production, future yield losses have been predicted for both major crops. When CO2 fertilization effects are taken into account significant yield gains are predicted for soybean, together with a shift in global production from the Southern to the Northern hemisphere. Maize production is also forecast to shift northwards. However, unless plant breeders are able to produce new hybrids with improved traits, the forecasted yield losses for maize will only be mitigated by agro-management adaptations. In addition, the increasing demands of a growing world population will require larger areas of marginal land to be used for maize and soybean production. We summarize the outputs of crop models, together with mitigation options for decreasing the negative impacts of climate on the global maize and soybean production, providing an overview of projected land-use change as a major determining factor for future global crop production.

Modeling perceptions of climatic risk in crop production.

In agricultural production, land-use decisions are components of economic planning that result in the strategic allocation of fields. Climate variability represents an uncertainty factor in crop production. Considering yield impact, climatic influence is perceived during and evaluated at the end of crop production cycles. In practice, this information is then incorporated into planning for the upcoming season. This process contributes to attitudes toward climate-induced risk in crop production. In the literature, however, the subjective valuation of risk is modeled as a risk attitude toward variations in (monetary) outcomes. Consequently, climatic influence may be obscured by political and market influences so that risk perceptions during the production process are neglected. We present a utility concept that allows the inclusion of annual risk scores based on mid-season risk perceptions that are incorporated into field-planning decisions. This approach is exemplified and implemented for winter wheat production in the Kraichgau, a region in Southwest Germany, using the integrated bio-economic simulation model FarmActor and empirical data from the region. Survey results indicate that a profitability threshold for this crop, the level of "still-good yield" (sgy), is 69 dt ha-1 (regional mean Kraichgau sample) for a given season. This threshold governs the monitoring process and risk estimators. We tested the modeled estimators against simulation results using ten projected future weather time series for winter wheat production. The mid-season estimators generally proved to be effective. This approach can be used to improve the modeling of planning decisions by providing a more comprehensive evaluation of field-crop response to climatic changes from an economic risk point of view. The methodology further provides economic insight in an agrometeorological context where prices for crops or inputs are lacking, but farmer attitudes toward risk should still be included in the analysis.

Foregone benefits of important food crop improvements in Sub-Saharan Africa.

A number of new crops have been developed that address important traits of particular relevance for smallholder farmers in Africa. Scientists, policy makers, and other stakeholders have raised concerns that the approval process for these new crops causes delays that are often scientifically unjustified. This article develops a real option model for the optimal regulation of a risky technology that enhances economic welfare and reduces malnutrition. We consider gradual adoption of the technology and show that delaying approval reduces uncertainty about perceived risks of the technology. Optimal conditions for approval incorporate parameters of the stochastic processes governing the dynamics of risk. The model is applied to three cases of improved crops, which either are, or are expected to be, delayed by the regulatory process. The benefits and costs of the crops are presented in a partial equilibrium that considers changes in adoption over time and the foregone benefits caused by a delay in approval under irreversibility and uncertainty. We derive the equilibrium conditions where the net-benefits of the technology equal the costs that would justify a delay. The sooner information about the safety of the technology arrive, the lower the costs for justifying a delay need to be i.e. it pays more to delay. The costs of a delay can be substantial: e.g. a one year delay in approval of the pod-borer resistant cowpea in Nigeria will cost the country about 33 million USD to 46 million USD and between 100 and 3,000 lives.

Crop yield response to climate change varies with crop spatial distribution pattern.

The linkage between crop yield and climate variability has been confirmed in numerous studies using statistical approaches. A crucial assumption in these studies is that crop spatial distribution pattern is constant over time. Here, we explore how changes in county-level corn spatial distribution pattern modulate the response of its yields to climate change at the state level over the Contiguous United States. Our results show that corn yield response to climate change varies with crop spatial distribution pattern, with distinct impacts on the magnitude and even the direction at the state level. Corn yield is predicted to decrease by 20~40% by 2050 s when considering crop spatial distribution pattern changes, which is 6~12% less than the estimates with fixed cropping pattern. The beneficial effects are mainly achieved by reducing the negative impacts of daily maximum temperature and strengthening the positive impacts of precipitation. Our results indicate that previous empirical studies could be biased in assessing climate change impacts by ignoring the changes in crop spatial distribution pattern. This has great implications for understanding the increasing debates on whether climate change will be a net gain or loss for regional agriculture.

A Risk Assessment Framework for Seed Degeneration: Informing an Integrated Seed Health Strategy for Vegetatively Propagated Crops.

Pathogen buildup in vegetative planting material, termed seed degeneration, is a major problem in many low-income countries. When smallholder farmers use seed produced on-farm or acquired outside certified programs, it is often infected. We introduce a risk assessment framework for seed degeneration, evaluating the relative performance of individual and combined components of an integrated seed health strategy. The frequency distribution of management performance outcomes was evaluated for models incorporating biological and environmental heterogeneity, with the following results. (1) On-farm seed selection can perform as well as certified seed, if the rate of success in selecting healthy plants for seed production is high; (2) when choosing among within-season management strategies, external inoculum can determine the relative usefulness of 'incidence-altering management' (affecting the proportion of diseased plants/seeds) and 'rate-altering management' (affecting the rate of disease transmission in the field); (3) under severe disease scenarios, where it is difficult to implement management components at high levels of effectiveness, combining management components can be synergistic and keep seed degeneration below a threshold; (4) combining management components can also close the yield gap between average and worst-case scenarios. We also illustrate the potential for expert elicitation to provide parameter estimates when empirical data are unavailable. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Low-Cost Microprocessor-Controlled Rotating Stage for Medium-Throughput Time-Lapse Plant Phenotyping.

Here we provide the instructions to build a cost-friendly rotating stage, which enables time-lapse phenotyping of seedlings, grown vertically on in vitro plates, in a medium-throughput manner.