Stomatal conductance reduction tradeoffs in maize leaves: A theoretical study

As the leading global grain crop, maize significantly impacts agricultural water usage. Presently, photosynthesis (A net ${A}_{\text{net}}$ ) in leaves of modern maize crops is saturated withCO 2 ${\text{CO}}_{2}$ , implying that reducing stomatal conductance (g s ${g}_{{\rm{s}}}$ ) would not affectA net ${A}_{\text{net}}$ but reduce transpiration (τ $\tau $ ), thereby increasing water use efficiency (WUE). Whileg s ${g}_{{\rm{s}}}$ reduction benefits upper canopy leaves under optimal conditions, the tradeoffs in low light and nitrogen-deficient leaves under nonoptimal microenvironments remain unexplored. Moreover,g s ${g}_{{\rm{s}}}$ reduction increases leaf temperature (T leaf ${T}_{\text{leaf}}$ ) and water vapor pressure deficit, partially counteracting transpiratory water savings. Therefore, the overall impact ofg s ${g}_{{\rm{s}}}$ reduction on water savings remains unclear. Here, we use a process-based leaf model to investigate the benefits of reducedg s ${g}_{{\rm{s}}}$ in maize leaves under different microenvironments. Our findings show that increases inT leaf ${T}_{\text{leaf}}$ due tog s ${g}_{{\rm{s}}}$ reduction can diminish WUE gains by up to 20%. However,g s ${g}_{{\rm{s}}}$ reduction still results in beneficial WUE tradeoffs, where a 29% decrease ing s ${g}_{{\rm{s}}}$ in upper canopy leaves results in a 28% WUE gain without loss inA net ${A}_{\text{net}}$ . Lower canopy leaves exhibit superior tradeoffs ing s ${g}_{{\rm{s}}}$ reduction with 178% gains in WUE without loss inA net ${A}_{\text{net}}$ . Our simulations show that these WUE benefits are resilient to climate change.

Branch angle and leaflet shape are associated with canopy coverage in soybean

Early canopy coverage is a desirable trait that is a major determinant of yield in soybean (Glycine max). Variation in traits comprising shoot architecture can influence canopy coverage, canopy light interception, canopy-level photosynthesis, and source-sink partitioning efficiency. However, little is known about the extent of phenotypic diversity of shoot architecture traits and their genetic control in soybean. Thus, we sought to understand the contribution of shoot architecture traits to canopy coverage and to determine the genetic control of these traits. We examined the natural variation for shoot architecture traits in a set of 399 diverse maturity group I soybean (SoyMGI) accessions to identify relationships between traits, and to identify loci that are associated with canopy coverage and shoot architecture traits. Canopy coverage was correlated with branch angle, number of branches, plant height, and leaf shape. Using previously collected 50K single nucleotide polymorphism data, we identified quantitative trait locus (QTL) associated with branch angle, number of branches, branch density, leaflet shape, days to flowering, maturity, plant height, number of nodes, and stem termination. In many cases, QTL intervals overlapped with previously described genes or QTL. We also found QTL associated with branch angle and leaflet shape located on chromosomes 19 and 4, respectively, and these QTL overlapped with QTL associated with canopy coverage, suggesting the importance of branch angle and leaflet shape in determining canopy coverage. Our results highlight the role individual architecture traits play in canopy coverage and contribute information on their genetic control that could help facilitate future efforts in their genetic manipulation.

Thermodynamic basis for the demarcation of Arctic and alpine treelines

At the edge of alpine and Arctic ecosystems all over the world, a transition zone exists beyond which it is either infeasible or unfavorable for trees to exist, colloquially identified as the treeline. We explore the possibility of a thermodynamic basis behind this demarcation in vegetation by considering ecosystems as open systems driven by thermodynamic advantage—defined by vegetation’s ability to dissipate heat from the earth’s surface to the air above the canopy. To deduce whether forests would be more thermodynamically advantageous than existing ecosystems beyond treelines, we construct and examine counterfactual scenarios in which trees exist beyond a treeline instead of the existing alpine meadow or Arctic tundra. Meteorological data from the Italian Alps, United States Rocky Mountains, and Western Canadian Taiga-Tundra are used as forcing for model computation of ecosystem work and temperature gradients at sites on both sides of each treeline with and without trees. Model results indicate that the alpine sites do not support trees beyond the treeline, as their presence would result in excessive CO$$_2$$ 2 loss and extended periods of snowpack due to temperature inversions (i.e., positive temperature gradient from the earth surface to the atmosphere). Further, both Arctic and alpine sites exhibit negative work resulting in positive feedback between vegetation heat dissipation and temperature gradient, thereby extending the duration of temperature inversions. These conditions demonstrate thermodynamic infeasibility associated with the counterfactual scenario of trees existing beyond a treeline. Thus, we conclude that, in addition to resource constraints, a treeline is an outcome of an ecosystem’s ability to self-organize towards the most advantageous vegetation structure facilitated by thermodynamic feasibility.

A Novel and Non-Invasive Approach to Evaluating Soil Moisture without Soil Disturbances: Contactless Ultrasonic System

Soil moisture has been considered a key variable in governing the terrestrial ecosystem. However, it is challenging to preserve indigenous soil characteristics using conventional soil moisture monitoring methods that require maximum soil contacts. To overcome this issue, we developed a non-destructive method of evaluating soil moisture using a contactless ultrasonic system. This system was designed to measure leaky Rayleigh waves at the air-soil joint-half space. The influences of soil moisture on leaky Rayleigh waves were explored under sand, silt, and clay in a controlled experimental design. Our results showed that there were strong relationships between the energy and amplitude of leaky Rayleigh waves and soil moisture for all three soil cases. These results can be explained by reduced soil strengths during evaporation processes for coarse soil particles as opposed to fine soil particles. To evaluate soil moisture based on the dynamic parameters and wave properties obtained from the observed leaky Rayleigh waves, we used the random forest model. The accuracy of predicted soil moisture was exceptional for test data sets under all soil types (R2 ≥ 0.98, RMSE ≤ 0.0089 m3 m-3). That is, our study demonstrated that the leaky Rayleigh waves had great potential to continuously assess soil moisture variations without soil disturbances.

Evaluating Ecohydrological Model Sensitivity to Input Variability with an Information-Theory-Based Approach

Ecohydrological models vary in their sensitivity to forcing data and use available information to different extents. We focus on the impact of forcing precision on ecohydrological model behavior particularly by quantizing, or binning, time-series forcing variables. We use rate-distortion theory to quantize time-series forcing variables to different precisions. We evaluate the effect of different combinations of quantized shortwave radiation, air temperature, vapor pressure deficit, and wind speed on simulated heat and carbon fluxes for a multi-layer canopy model, which is forced and validated with eddy covariance flux tower observation data. We find that the model is more sensitive to radiation than meteorological forcing input, but model responses also vary with seasonal conditions and different combinations of quantized inputs. While any level of quantization impacts carbon flux similarly, specific levels of quantization influence heat fluxes to different degrees. This study introduces a method to optimally simplify forcing time series, often without significantly decreasing model performance, and could be applied within a sensitivity analysis framework to better understand how models use available information.

Assessing diversity in canopy architecture, photosynthesis, and water-use efficiency in a cowpea magic population

Optimizing crops to improve light absorption and CO2 assimilation throughout the canopy is a proposed strategy to increase yield and meet the needs of a growing population by 2050. Globally, the greatest population increase is expected to occur in Sub-Saharan Africa where large yield gaps currently persist; therefore, it is crucial to develop high-yielding crops adapted to this region. In this study, we screened 50 cowpea (Vigna unguiculata (L.) Walp) genotypes from the multi-parent advanced generation inter-cross (MAGIC) population for canopy architectural traits, canopy photosynthesis, and water-use efficiency using a canopy gas exchange chamber in order to improve our understanding of the relationships among those traits. Canopy architecture contributed to 38.6% of the variance observed in canopy photosynthesis. The results suggest that the light environment within the canopy was a limiting factor for canopy CO2 assimilation. Traits favoring greater exposure of leaf area to light such as the width of the canopy relative to the total leaf area were associated with greater canopy photosynthesis, especially in canopies with high biomass. Canopy water-use efficiency was highly determined by canopy photosynthetic activity and therefore canopy architecture, which indicates that optimizing the canopy will also contribute to improving canopy water-use efficiency. We discuss different breeding strategies for future programs aimed at the improvement of cowpea yield for the Sub-Saharan African region. We show that breeding for high biomass will not optimize canopy CO2 assimilation and suggest that selection should include multiple canopy traits to improve light penetration.

Discerning the thermodynamic feasibility of the spontaneous coexistence of multiple functional vegetation groups

Can the Second Law of Thermodynamics explain why ecosystems naturally organize into a complex structure composed of multiple vegetation species and functional groups? Ecosystem structure, which refers to the number and type of plant functional groups, is the result of self-organization, or the spontaneous emergence of order from random fluctuations. By considering ecosystems as open thermodynamic systems, we model and study these fluctuations of throughput signatures on short timescales to determine the drivers and characteristics of ecosystem structure. This diagnostic approach allows us to use fluxes of energy and entropy to calculate an ecosystem's estimated work and understand the thermodynamic behavior of the system. We use a multi-layer canopy-root-soil model to calculate the energy and entropy fluxes of different scenarios for field sites across various climates. At each site, scenarios comprised of native individual plant functional groups and a coexisting multi-group composition scenario including all functional groups observed at the site are compared. Ecosystem-scale calculations demonstrate that entropy fluxes and work efficiency-the work performed for the amount of radiation entering the ecosystem-are greatest in the multi-group scenario when its leaf area is significantly larger than each of its individual functional groups. Thus, we conclude that ecosystems self-organize towards the vegetation structure with the greatest outgoing entropy flux and work efficiency, resulting in the coexistence of multiple functional groups and performing the maximum amount of work within the constraints of locally available energy, water, and nutrients.

An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources

The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the abovementioned scientific questions by combining medium-resolution (10 m–1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms. This paper provides a general overview of the iAqueduct theoretical framework and introduces some preliminary results.

Dry Season Irrigation Promotes Leaf Growth in Eucalyptus urophylla × E. grandis under Fertilization

Leaves are essential for photosynthesis and gas exchange, and their growth characteristics are the key factors that influence the carbon budget. Eucalyptus is widely afforested in south China due to its fast-growing and high-yield features. Water and fertilizer are the main factors affecting plant growth. Studying the effects of different water and fertilizer treatments on the growth of Eucalyptus leaves under seasonal drought could further elucidate the optimal additions for Eucalyptus productivity. In this study, we investigated the leaf area, length, width, perimeter, and expansion rates of the commercial species E. urophylla × E. grandis under different treatments of dry season irrigation and fertilizer application to elucidate the growth dynamics of the leaves. The results indicated that both dry season irrigation and fertilizer could affect whole leaf expansion. Leaf area was largest when water and fertilizer were added at the same time. In this experiment, we found that fertilization had a significant effect on the leaf shape index of the Eucalyptus leaves. The leaf shape index was larger with the fertilizer treatment, which made the leaves slender. Dry season irrigation shorten the peak period of leaf growth and increase the leaf area. Our results help to further understand the mechanism of Eucalyptus productivity under seasonal drought and provide theoretical support for Eucalyptus production.

Increasing canopy photosynthesis in rice can be achieved without a large increase in water use-A model based on free-air CO2 enrichment

Achieving higher canopy photosynthesis rates is one of the keys to increasing future crop production; however, this typically requires additional water inputs because of increased water loss through the stomata. Lowland rice canopies presently consume a large amount of water, and any further increase in water usage may significantly impact local water resources. This situation is further complicated by changing the environmental conditions such as rising atmospheric CO₂ concentration ([CO₂ ]). Here, we modeled and compared evapotranspiration of fully developed rice canopies of a high-yielding rice cultivar (Oryza sativa L. cv. Takanari) with a common cultivar (cv. Koshihikari) under ambient and elevated [CO₂ ] (A-CO₂ and E-CO₂ , respectively) via leaf ecophysiological parameters derived from a free-air CO₂ enrichment (FACE) experiment. Takanari had 4%-5% higher evapotranspiration than Koshihikari under both A-CO₂ and E-CO₂ , and E-CO₂ decreased evapotranspiration of both varieties by 4%-6%. Therefore, if Takanari was cultivated under future [CO₂ ] conditions, the cost for water could be maintained at the same level as for cultivating Koshihikari at current [CO₂ ] with an increase in canopy photosynthesis by 36%. Sensitivity analyses determined that stomatal conductance was a significant physiological factor responsible for the greater canopy photosynthesis in Takanari over Koshihikari. Takanari had 30%-40% higher stomatal conductance than Koshihikari; however, the presence of high aerodynamic resistance in the natural field and lower canopy temperature of Takanari than Koshihikari resulted in the small difference in evapotranspiration. Despite the small difference in evapotranspiration between varieties, the model simulations showed that Takanari clearly decreased canopy and air temperatures within the planetary boundary layer compared to Koshihikari. Our results indicate that lowland rice varieties characterized by high-stomatal conductance can play a key role in enhancing productivity and moderating heat-induced damage to grain quality in the coming decades, without significantly increasing crop water use.

Decreasing, not increasing, leaf area will raise crop yields under global atmospheric change

Without new innovations, present rates of increase in yields of food crops globally are inadequate to meet the projected rising food demand for 2050 and beyond. A prevailing response of crops to rising [CO2 ] is an increase in leaf area. This is especially marked in soybean, the world's fourth largest food crop in terms of seed production, and the most important vegetable protein source. Is this increase in leaf area beneficial, with respect to increasing yield, or is it detrimental? It is shown from theory and experiment using open-air whole-season elevation of atmospheric [CO2 ] that it is detrimental not only under future conditions of elevated [CO2 ] but also under today's [CO2 ]. A mechanistic biophysical and biochemical model of canopy carbon exchange and microclimate (MLCan) was parameterized for a modern US Midwest soybean cultivar. Model simulations showed that soybean crops grown under current and elevated (550 [ppm]) [CO2 ] overinvest in leaves, and this is predicted to decrease productivity and seed yield 8% and 10%, respectively. This prediction was tested in replicated field trials in which a proportion of emerging leaves was removed prior to expansion, so lowering investment in leaves. The experiment was conducted under open-air conditions for current and future elevated [CO2 ] within the Soybean Free Air Concentration Enrichment facility (SoyFACE) in central Illinois. This treatment resulted in a statistically significant 8% yield increase. This is the first direct proof that a modern crop cultivar produces more leaf than is optimal for yield under today's and future [CO2 ] and that reducing leaf area would give higher yields. Breeding or bioengineering for lower leaf area could, therefore, contribute very significantly to meeting future demand for staple food crops given that an 8% yield increase across the USA alone would amount to 6.5 million metric tons annually.

The Costs of Photorespiration to Food Production Now and in the Future

Photorespiration is essential for C3 plants but operates at the massive expense of fixed carbon dioxide and energy. Photorespiration is initiated when the initial enzyme of photosynthesis, ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco), reacts with oxygen instead of carbon dioxide and produces a toxic compound that is then recycled by photorespiration. Photorespiration can be modeled at the canopy and regional scales to determine its cost under current and future atmospheres. A regional-scale model reveals that photorespiration currently decreases US soybean and wheat yields by 36% and 20%, respectively, and a 5% decrease in the losses due to photorespiration would be worth approximately $500 million annually in the United States. Furthermore, photorespiration will continue to impact yield under future climates despite increases in carbon dioxide, with models suggesting a 12-55% improvement in gross photosynthesis in the absence of photorespiration, even under climate change scenarios predicting the largest increases in atmospheric carbon dioxide concentration. Although photorespiration is tied to other important metabolic functions, the benefit of improving its efficiency appears to outweigh any potential secondary disadvantages.

Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species

The morphological and biochemical properties of plant canopies are strong predictors of photosynthetic capacity and nutrient cycling. Remote sensing research at the leaf and canopy scales has demonstrated the ability to characterize the biochemical status of vegetation canopies using reflectance spectroscopy, including at the leaf level and canopy level from air- and spaceborne imaging spectrometers. We developed a set of accurate and precise spectroscopic calibrations for the determination of leaf chemistry (contents of nitrogen, carbon, and fiber constituents), morphology (leaf mass per area, Marea), and isotopic composition (δ15N) of temperate and boreal tree species using spectra of dried and ground leaf material. The data set consisted of leaves from both broadleaf and needle-leaf conifer species and displayed a wide range in values, determined with standard analytical approaches: 0.7–4.4% for nitrogen (Nmass), 42–54% for carbon (Cmass), 17–58% for fiber (acid-digestible fiber, ADF), 7–44% for lignin (acid-digestible lignin, ADL), 3–31% for cellulose, 17–265 g/m2 for Marea, and −9.4‰ to 0.8‰ for δ15N. The calibrations were developed using a partial least-squares regression (PLSR) modeling approach combined with a novel uncertainty analysis. Our PLSR models yielded model calibration (independent validation shown in parentheses) R2 and the root mean square error (RMSE) values, respectively, of 0.98 (0.97) and 0.10% (0.13%) for Nmass, R2 = 0.77 (0.73) and RMSE = 0.88% (0.95%) for Cmass, R2 = 0.89 (0.84) and RMSE = 2.8% (3.4%) for ADF, R2 = 0.77 (0.69) and RMSE = 2.4% (3.9%) for ADL, R2 = 0.77 (0.72) and RMSE = 1.4% (1.9%) for leaf cellulose, R2 = 0.62 (0.60) and RMSE = 0.91‰ (1.5‰) for δ15N, and R2 = 0.88 (0.87) with RMSE = 17.2 g/m2 (22.8 g/m2) for Marea. This study demonstrates the potential for rapid and accurate estimation of key foliar traits of forest canopies that are important for ecological research and modeling activities, with a single calibration equation valid over a wide range of northern temperate and boreal species and leaf physiognomies. The results provide the basis to characterize important variability between and within species, and across ecological gradients using a rapid, cost-effective, easily replicated method.

Terrestrial ecosystems in a changing environment: a dominant role for water

Transpiration--the movement of water from the soil, through plants, and into the atmosphere--is the dominant water flux from the earth's terrestrial surface. The evolution of vascular plants, while increasing terrestrial primary productivity, led to higher transpiration rates and widespread alterations in the global climate system. Similarly, anthropogenic influences on transpiration rates are already influencing terrestrial hydrologic cycles, with an even greater potential for changes lying ahead. Intricate linkages among anthropogenic activities, terrestrial productivity, the hydrologic cycle, and global demand for ecosystem services will lead to increased pressures on ecosystem water demands. Here, we focus on identifying the key drivers of ecosystem water use as they relate to plant physiological function, the role of predicted global changes in ecosystem water uses, trade-offs between ecosystem water use and carbon uptake, and knowledge gaps.

Modelling C₃ photosynthesis from the chloroplast to the ecosystem

Globally, photosynthesis accounts for the largest flux of CO₂ from the atmosphere into ecosystems and is the driving process for terrestrial ecosystem function. The importance of accurate predictions of photosynthesis over a range of plant growth conditions led to the development of a C₃ photosynthesis model by Farquhar, von Caemmerer & Berry that has become increasingly important as society places greater pressures on vegetation. The photosynthesis model has played a major role in defining the path towards scientific understanding of photosynthetic carbon uptake and the role of photosynthesis on regulating the earth's climate and biogeochemical systems. In this review, we summarize the photosynthesis model, including its continued development and applications. We also review the implications these developments have on quantifying photosynthesis at a wide range of spatial and temporal scales, and discuss the model's role in determining photosynthetic responses to changes in environmental conditions. Finally, the review includes a discussion of the larger-scale modelling and remote-sensing applications that rely on the leaf photosynthesis model and are likely to open new scientific avenues to address the increasing challenges to plant productivity over the next century.

Analytical expressions of variability in ecosystem structure and function obtained from three-dimensional stochastic vegetation modelling

Whole ecosystem exchange of water, carbon and energy is predominately determined by complex leaf-level processes occurring at individual plants. Interaction between individuals results in a distribution of environmental conditions that drive a variety of nonlinear response functions such as transpiration and photosynthesis. The nonlinearity of biophysical processes requires higher-order statistical descriptions of micro-environment distributions in order to accurately determine the landscape-scale mean functional response. We present a mathematical framework for describing vegetation structure based on the density, dispersion, size distribution and allometry of individuals within a landscape. Using three-dimensional stochastic vegetation modelling, we develop analytic expressions of the second-order statistics of vegetation canopies, namely the mean and variance of leaf area density and leaf area index with height. These expressions also allow for the approximation of the distribution of beam penetration and sunfleck statistics through the canopy as a function of height. Finally, we demonstrate how landscape-scale fluxes are strongly affected by the variability in canopy micro-environments, and how stochastic vegetation modelling improves flux estimates relative to traditional homogeneous canopy models.

Implications for the hydrologic cycle under climate change due to the expansion of bioenergy crops in the Midwestern United States

To meet emerging bioenergy demands, significant areas of the large-scale agricultural landscape of the Midwestern United States could be converted to second generation bioenergy crops such as miscanthus and switchgrass. The high biomass productivity of bioenergy crops in a longer growing season linked tightly to water use highlight the potential for significant impact on the hydrologic cycle in the region. This issue is further exacerbated by the uncertainty in the response of the vegetation under elevated CO(2) and temperature. We use a mechanistic multilayer canopy-root-soil model to (i) capture the eco-physiological acclimations of bioenergy crops under climate change, and (ii) predict how hydrologic fluxes are likely to be altered from their current magnitudes. Observed data and Monte Carlo simulations of weather for recent past and future scenarios are used to characterize the variability range of the predictions. Under present weather conditions, miscanthus and switchgrass utilized more water than maize for total seasonal evapotranspiration by approximately 58% and 36%, respectively. Projected higher concentrations of atmospheric CO(2) (550 ppm) is likely to decrease water used for evapotranspiration of miscanthus, switchgrass, and maize by 12%, 10%, and 11%, respectively. However, when climate change with projected increases in air temperature and reduced summer rainfall are also considered, there is a net increase in evapotranspiration for all crops, leading to significant reduction in soil-moisture storage and specific surface runoff. These results highlight the critical role of the warming climate in potentially altering the water cycle in the region under extensive conversion of existing maize cropping to support bioenergy demand.