Improved net carbon budgets in the US Midwest through direct measured impacts of enhanced weathering.

Terrestrial enhanced weathering (EW) through the application of Mg- or Ca-rich rock dust to soil is a negative emission technology with the potential to address impacts of climate change. The effectiveness of EW was tested over 4 years by spreading ground basalt (50 t ha-1 year-1 ) on maize/soybean and miscanthus cropping systems in the Midwest US. The major elements of the carbon budget were quantified through measurements of eddy covariance, soil carbon flux, and biomass. The movement of Mg and Ca to deep soil, released by weathering, balanced by a corresponding alkalinity flux, was used to measure the drawdown of CO2 , where the release of cations from basalt was measured as the ratio of rare earth elements to base cations in the applied rock dust and in the surface soil. Basalt application stimulated peak biomass and net primary production in both cropping systems and caused a small but significant stimulation of soil respiration. Net ecosystem carbon balance (NECB) was strongly negative for maize/soybean (-199 to -453 g C m-2 year-1 ) indicating this system was losing carbon to the atmosphere. Average EW (102 g C m-2 year-1 ) offset carbon loss in the maize/soybean by 23%-42%. NECB of miscanthus was positive (63-129 g C m-2 year-1 ), indicating carbon gain in the system, and EW greatly increased inorganic carbon storage by an additional 234 g C m-2 year-1 . Our analysis indicates a co-deployment of a perennial biofuel crop (miscanthus) with EW leads to major wins-increased harvested yields of 29%-42% with additional carbon dioxide removal (CDR) of 8.6 t CO2 ha-1 year-1 . EW applied to maize/soybean drives a CDR of 3.7 t CO2 ha-1 year-1 , which partially offsets well-established carbon losses from soil from this crop rotation. EW applied in the US Midwest creates measurable improvements to the carbon budgets perennial bioenergy crops and conventional row crops.

Short- and long-term warming events on photosynthetic physiology, growth, and yields of field grown crops.

Global temperatures are rising from increasing concentrations of greenhouse gases in the atmosphere associated with anthropogenic activities. Global warming includes a warmer shift in mean temperatures as well as increases in the probability of extreme heating events, termed heat waves. Despite the ability of plants to cope with temporal variations in temperature, global warming is increasingly presenting challenges to agroecosystems. The impact of warming on crop species has direct consequences on food security, therefore understanding impacts and opportunities to adapt crops to global warming necessitates experimentation that allows for modification of growth environments to represent global warming scenarios. Published studies addressing crop responses to warming are extensive, however, in-field studies where growth temperature is manipulated to mimic global warming are limited. Here, we provide an overview of in-field heating techniques employed to understand crop responses to warmer growth environments. We then focus on key results associated with season-long warming, as expected with rising global mean temperatures, and with heat waves, as a consequence of increasing temperature variability and rising global mean temperatures. We then discuss the role of rising temperatures on atmospheric water vapor pressure deficit and potential implications for crop photosynthesis and productivity. Finally, we review strategies by which crop photosynthetic processes might be optimized to adapt crops to the increasing temperatures and frequencies of heat waves. Key findings from this review are that higher temperatures consistently reduce photosynthesis and yields of crops even as atmospheric carbon dioxide increases, yet potential strategies to minimize losses from high-temperature exist.

Two decades of fumigation data from the Soybean Free Air Concentration Enrichment facility.

The Soybean Free Air Concentration Enrichment (SoyFACE) facility is the longest running open-air carbon dioxide and ozone enrichment facility in the world. For over two decades, soybean, maize, and other crops have been exposed to the elevated carbon dioxide and ozone concentrations anticipated for late this century. The facility, located in East Central Illinois, USA, exposes crops to different atmospheric concentrations in replicated octagonal ~280 m2 Free Air Concentration Enrichment (FACE) treatment plots. Each FACE plot is paired with an untreated control (ambient) plot. The experiment provides important ground truth data for predicting future crop productivity. Fumigation data from SoyFACE were collected every four seconds throughout each growing season for over two decades. Here, we organize, quality control, and collate 20 years of data to facilitate trend analysis and crop modeling efforts. This paper provides the rationale for and a description of the SoyFACE experiments, along with a summary of the fumigation data and collation process, weather and ambient data collection procedures, and explanations of air pollution metrics and calculations.

Reductions in leaf area index, pod production, seed size, and harvest index drive yield loss to high temperatures in soybean.

Improvements in genetics, technology, and agricultural intensification have increased soybean yields; however, adverse climate conditions may prevent these gains from being fully realized in the future. Higher growing season temperatures reduce soybean yields in key production regions including the US Midwest, and better understanding of the developmental and physiological mechanisms that constrain soybean yield under high temperature conditions is needed. This study tested the response of two soybean cultivars to four elevated temperature treatments (+1.7, +2.6, +3.6, and +4.8 °C) in the field over three growing seasons and identified threshold temperatures for response and linear versus non-linear trait responses to temperature. Yield declined non-linearly to temperature, with decreases apparent when canopy temperature exceeded 20.9 °C for the locally adapted cultivar and 22.7°C for a cultivar adapted to more southern locations. While stem node number increased with increasing temperature, leaf area index decreased substantially. Pod production, seed size, and harvest index significantly decreased with increasing temperature. The seasonal average temperature of even the mildest treatment exceeded the threshold temperatures for yield loss, emphasizing the importance of improving temperature tolerance in soybean germplasm with intensifying climate change.

Leaf, plant, to canopy: A mechanistic study on aboveground plasticity and plant density within a maize-soybean intercrop system for the Midwest, USA.

Plants have evolved to adapt to their neighbours through plastic trait responses. In intercrop systems, plant growth occurs at different spatial and temporal dimensions, creating a competitive light environment where aboveground plasticity may support complementarity in light-use efficiency, realizing yield gains per unit area compared with monoculture systems. Physiological and architectural plasticity including the consequences for light-use efficiency and yield in a maize-soybean solar corridor intercrop system was compared, empirically, with the standard monoculture systems of the Midwest, USA. The impact of reducing maize plant density on yield was investigated in the following year. Intercropped maize favoured physiological plasticity over architectural plasticity, which maintained harvest index (HI) but reduced light interception efficiency (ɛi ) and conversion efficiency (ɛc ). Intercropped soybean invested in both plasticity responses, which maintained ɛi , but HI and ɛc decreased. Reducing maize plant density within the solar corridor rows did not improve yields under monoculture and intercrop systems. Overall, the intercrop decreased land-use efficiency by 9%-19% and uncoordinated investment in aboveground plasticity by each crop under high maize plant density does not support complementarity in light-use efficiency. Nonetheless, the mechanistic understanding gained from this study may improve crop cultivars and intercrop designs for the Midwest to increase yield.

To have value, comparisons of high-throughput phenotyping methods need statistical tests of bias and variance.

The gap between genomics and phenomics is narrowing. The rate at which it is narrowing, however, is being slowed by improper statistical comparison of methods. Quantification using Pearson's correlation coefficient (r) is commonly used to assess method quality, but it is an often misleading statistic for this purpose as it is unable to provide information about the relative quality of two methods. Using r can both erroneously discount methods that are inherently more precise and validate methods that are less accurate. These errors occur because of logical flaws inherent in the use of r when comparing methods, not as a problem of limited sample size or the unavoidable possibility of a type I error. A popular alternative to using r is to measure the limits of agreement (LOA). However both r and LOA fail to identify which instrument is more or less variable than the other and can lead to incorrect conclusions about method quality. An alternative approach, comparing variances of methods, requires repeated measurements of the same subject, but avoids incorrect conclusions. Variance comparison is arguably the most important component of method validation and, thus, when repeated measurements are possible, variance comparison provides considerable value to these studies. Statistical tests to compare variances presented here are well established, easy to interpret and ubiquitously available. The widespread use of r has potentially led to numerous incorrect conclusions about method quality, hampering development, and the approach described here would be useful to advance high throughput phenotyping methods but can also extend into any branch of science. The adoption of the statistical techniques outlined in this paper will help speed the adoption of new high throughput phenotyping techniques by indicating when one should reject a new method, outright replace an old method or conditionally use a new method.

Increased bundle-sheath leakiness of CO2 during photosynthetic induction shows a lack of coordination between the C4 and C3 cycles.

Use of a complete dynamic model of NADP-malic enzyme C4 photosynthesis indicated that, during transitions from dark or shade to high light, induction of the C4 pathway was more rapid than that of C3 , resulting in a predicted transient increase in bundle-sheath CO2 leakiness (ϕ). Previously, ϕ has been measured at steady state; here we developed a new method, coupling a tunable diode laser absorption spectroscope with a gas-exchange system to track ϕ in sorghum and maize through the nonsteady-state condition of photosynthetic induction. In both species, ϕ showed a transient increase to > 0.35 before declining to a steady state of 0.2 by 1500 s after illumination. Average ϕ was 60% higher than at steady state over the first 600 s of induction and 30% higher over the first 1500 s. The transient increase in ϕ, which was consistent with model prediction, indicated that capacity to assimilate CO2 into the C3 cycle in the bundle sheath failed to keep pace with the rate of dicarboxylate delivery by the C4 cycle. Because nonsteady-state light conditions are the norm in field canopies, the results suggest that ϕ in these major crops in the field is significantly higher and energy conversion efficiency lower than previous measured values under steady-state conditions.

Removal of redox-sensitive Rubisco Activase does not alter Rubisco regulation in soybean.

Rubisco activase (Rca) facilitates the catalytic repair of Rubisco, the CO2-fixing enzyme of photosynthesis, following periods of darkness, low to high light transitions or stress. Removal of the redox-regulated isoform of Rubisco activase, Rca-α, enhances photosynthetic induction in Arabidopsis and has been suggested as a strategy for the improvement of crops, which may experience frequent light transitions in the field; however, this has never been tested in a crop species. Therefore, we used RNAi to reduce the Rca-α content of soybean (Glycine max cv. Williams 82) below detectable levels and then characterized the growth, photosynthesis, and Rubisco activity of the resulting transgenics, in both growth chamber and field conditions. Under a 16 h sine wave photoperiod, the reduction of Rca-α contents had no impact on morphological characteristics, leaf expansion rate, or total biomass. Photosynthetic induction rates were unaltered in both chamber-grown and field-grown plants. Plants with reduced Rca-α content maintained the ability to regulate Rubisco activity in low light just as in control plants. This result suggests that in soybean, Rca-α is not as centrally involved in the regulation of Rca oligomer activity as it is in Arabidopsis. The isoform stoichiometry supports this conclusion, as Rca-α comprises only ~ 10% of the Rubisco activase content of soybean, compared to ~ 50% in Arabidopsis. This is likely to hold true in other species that contain a low ratio of Rca-α to Rca-ß isoforms.

Drought imprints on crops can reduce yield loss: Nature's insights for food security.

The Midwestern "Corn-Belt" in the United States is the most productive agricultural region on the planet despite being predominantly rainfed. In this region, global climate change is driving precipitation patterns toward wetter springs and drier mid- to late-summers, a trend that is likely to intensify in the future. The lack of precipitation can lead to crop water limitations that ultimately impact growth and yields. Young plants exposed to water stress will often invest more resources into their root systems, possibly priming the crop for any subsequent mid- or late-season drought. The trend toward wetter springs, however, suggests that opportunities for crop priming may lessen in the future. Here, we test the hypothesis that early season dry conditions lead to drought priming in field-grown crops and this response will protect crops against growth and yield losses from late-season droughts. This hypothesis was tested for the two major Midwestern crop, maize and soybean, using high-resolution daily weather data, satellite-derived phenological metrics, field yield data, and ecosystem-scale model (Agricultural Production System Simulator) simulations. The results from this study showed that priming mitigated yield losses from a late season drought of up to 4.0% and 7.0% for maize and soybean compared with unprimed crops experiencing a late season drought. These results suggest that if the trend toward wet springs with drier summers continues, the relative impact of droughts on crop productivity is likely to worsen. Alternatively, identifying opportunities to breed or genetically modify pre-primed crop species may provide improved resilience to future climate change.

High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean (Glycine max) nested association mapping population.

Photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500-2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R2 from 0.56 to 0.96 and root mean square error approximately <10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis.

Advances in field-based high-throughput photosynthetic phenotyping.

Gas exchange techniques revolutionized plant research and advanced understanding, including associated fluxes and efficiencies, of photosynthesis, photorespiration, and respiration of plants from cellular to ecosystem scales. These techniques remain the gold standard for inferring photosynthetic rates and underlying physiology/biochemistry, although their utility for high-throughput phenotyping (HTP) of photosynthesis is limited both by the number of gas exchange systems available and the number of personnel available to operate the equipment. Remote sensing techniques have long been used to assess ecosystem productivity at coarse spatial and temporal resolutions, and advances in sensor technology coupled with advanced statistical techniques are expanding remote sensing tools to finer spatial scales and increasing the number and complexity of phenotypes that can be extracted. In this review, we outline the photosynthetic phenotypes of interest to the plant science community and describe the advances in high-throughput techniques to characterize photosynthesis at spatial scales useful to infer treatment or genotypic variation in field-based experiments or breeding trials. We will accomplish this objective by presenting six lessons learned thus far through the development and application of proximal/remote sensing-based measurements and the accompanying statistical analyses. We will conclude by outlining what we perceive as the current limitations, bottlenecks, and opportunities facing HTP of photosynthesis.

Development of a data-assimilation system to forecast agricultural systems: A case study of constraining soil water and soil nitrogen dynamics in the APSIM model.

As we face today's large-scale agricultural issues, the need for robust methods of agricultural forecasting has never been clearer. Yet, the accuracy and precision of our forecasts remains limited by current tools and methods. To overcome the limitations of process-based models and observed data, we iteratively designed and tested a generalizable and robust data-assimilation system that systematically constrains state variables in the APSIM model to improve forecast accuracy and precision. Our final novel system utilizes the Ensemble Kalman Filter to constrain model states and update model parameters at observed time steps and incorporates an algorithm that improves system performance through the joint estimation of system error matrices. We tested this system at the Energy Farm, a well-monitored research site in central Illinois, where we assimilated observed in situ soil moisture at daily time steps for two years and evaluated how assimilation impacted model forecasts of soil moisture, yield, leaf area index, tile flow, and nitrate leaching by comparing estimates with in situ observations. The system improved the accuracy and precision of soil moisture estimates for the assimilation layers by an average of 42% and 48%, respectively, when compared to the free model. Such improvements led to changes in the model's soil water and nitrogen processes and, on average, increased accuracy in forecasts of annual tile flow by 43% and annual nitrate loads by 10%. Forecasts of aboveground measures did not dramatically change with assimilation, a fact which highlights the limited potential of soil moisture as a constraint for a site with no water stress. Extending the scope of previous work, our results demonstrate the power of data assimilation to constrain important model estimates beyond the assimilated state variable, such as nitrate leaching. Replication of this study is necessary to further define the limitations and opportunities of the developed system.

Alternative pathway to photorespiration protects growth and productivity at elevated temperatures in a model crop.

Adapting crops to warmer growing season temperatures is a major challenge in mitigating the impacts of climate change on crop production. Warming temperatures drive greater evaporative demand and can directly interfere with both reproductive and vegetative physiological processes. Most of the world's crop species have C3 photosynthetic metabolism for which increasing temperature means higher rates of photorespiration, wherein the enzyme responsible for fixing CO2 fixes O2 instead followed by an energetically costly recycling pathway that spans several cell compartments. In C3 crops like wheat, rice and soybean, photorespiration translates into large yield losses that are predicted to increase as global temperature warms. Engineering less energy-intensive alternative photorespiratory pathways into crop chloroplasts drives increases in C3 biomass production under agricultural field conditions, but the efficacy of these pathways in mitigating the impact of warmer growing temperatures has not been tested. We grew tobacco plants expressing an alternative photorespiratory pathway under current and elevated temperatures (+5 °C) in agricultural field conditions. Engineered plants exhibited higher photosynthetic quantum efficiency under heated conditions than the control plants, and produced 26% (between 16% and 37%) more total biomass than WT plants under heated conditions, compared to 11% (between 5% and 17%) under ambient conditions. That is, engineered plants sustained 19% (between 11% and 21%) less yield loss under heated conditions compared to non-engineered plants. These results support the theoretical predictions of temperature impacts on photorespiratory losses and provide insight toward the optimisation strategies required to help sustain or improve C3 crop yields in a warming climate.

Predicting biochemical acclimation of leaf photosynthesis in soybean under in-field canopy warming using hyperspectral reflectance.

Traditional gas exchange measurements are cumbersome, which makes it difficult to capture variation in biochemical parameters, namely the maximum rate of carboxylation measured at a reference temperature (Vcmax25 ) and the maximum electron transport at a reference temperature (Jmax25 ), in response to growth temperature over time from days to weeks. Hyperspectral reflectance provides reliable measures of Vcmax25 and Jmax25 ; however, the capability of this method to capture biochemical acclimations of the two parameters to high growth temperature over time has not been demonstrated. In this study, Vcmax25 and Jmax25 were measured over multiple growth stages during two growing seasons for field-grown soybeans using both gas exchange techniques and leaf spectral reflectance under ambient and four elevated canopy temperature treatments (ambient+1.5, +3, +4.5, and +6°C). Spectral vegetation indices and machine learning methods were used to build predictive models for Vcmax25 and Jmax25 , based on the leaf reflectance. Results showed that these models yielded an R2 of 0.57-0.65 and 0.48-0.58 for Vcmax25 and Jmax25 , respectively. Hyperspectral reflectance captured biochemical acclimation of leaf photosynthesis to high temperature in the field, improving spatial and temporal resolution in the ability to assess the impact of future warming on crop productivity.

Photosynthesis, yield, energy balance, and water-use of intercropped maize and soybean.

By 2050, the U.S. Corn Belt will likely face a 23% increase in leaf-to-air vapor pressure deficit (VPDL), the driving force of evapotranspiration (ET), which may restrict maize yield improvements for rainfed agroecosystems. Alternative cropping systems, such as maize and legume intercrops, have previously demonstrated yield and resource-use advantages over monocultures. In this study, the residual energy balance approach was used to gain insights into how an additive simultaneous maize and soybean intercrop system regulates ET and water-use efficiency (WUE) compared to standard maize and soybean monoculture systems of the U.S. Corn Belt. Experimental field plots were rain-fed and arranged in a randomized complete block design in three blocks. Photosynthetic capacity and grain yield of maize were conserved in the intercrop. However, its competitive dominance shaded 80%-90% of incident light for intercropped soybean at canopy closure, leading to a 94% decrease in grain yield compared to soybean monoculture. The total grain yield per unit area of the additive intercrop (land-use efficiency) increased by 11% ± 6% (1 SE). Compared to maize monoculture, the intercrop had higher latent heat fluxes (λET) at night but lower daytime λET as the intercrop canopy surface temperature was approximately .25°C warmer, partitioning more energy to sensible heat flux. However, the diel differences in λET fluxes were not sufficient to establish a statistically significant or biologically relevant decrease in seasonal water-use (ΣET). Likewise, the increase in land-use efficiency by the intercrop was not sufficient to establish an increase in seasonal water-use efficiency. Intercropping high-performing maize and soybean cultivars in a dense configuration without negative impact suggests that efforts to increase yield and WUE may lead to improved benefits.

A review of transformative strategies for climate mitigation by grasslands.

Grasslands can significantly contribute to climate mitigation. However, recent trends indicate that human activities have switched their net cooling effect to a warming effect due to management intensification and land conversion. This indicates an urgent need for strategies directed to mitigate climate warming while enhancing productivity and efficiency in the use of land and natural (nutrients, water) resources. Here, we examine the potential of four innovative strategies to slow climate change including: 1) Adaptive multi-paddock grazing that consists of mimicking how ancestral herds roamed the Earth; 2) Agrivoltaics that consists of simultaneously producing food and energy from solar panels on the same land area; 3) Agroforestry with a reverse phenology tree species, Faidherbia (Acacia) albida, that has the unique trait of being photosynthetically active when intercropped herbaceous plants are dormant; and, 4) Enhanced Weathering, a negative emission technology that removes atmospheric CO2 from the atmosphere. Further, we speculate about potential unknown consequences of these different management strategies and identify gaps in knowledge. We find that all these strategies could promote at least some of the following benefits of grasslands: CO2 sequestration, non-CO2 GHG mitigation, productivity, resilience to climate change, and an efficient use of natural resources. However, there are obstacles to be overcome. Mechanistic assessment of the ecological, environmental, and socio-economic consequences of adopting these strategies at large scale are urgently needed to fully assess the potential of grasslands to provide food, energy and environmental security.

Can improved canopy light transmission ameliorate loss of photosynthetic efficiency in the shade? An investigation of natural variation in Sorghum bicolor.

Previous studies have found that maximum quantum yield of CO2 assimilation (Φ CO2,max,app) declines in lower canopies of maize and miscanthus, a maladaptive response to self-shading. These observations were limited to single genotypes, leaving it unclear whether the maladaptive shade response is a general property of this C4 grass tribe, the Andropogoneae. We explored the generality of this maladaptation by testing the hypothesis that erect leaf forms (erectophiles), which allow more light into the lower canopy, suffer less of a decline in photosynthetic efficiency than drooping leaf (planophile) forms. On average, Φ CO2,max,app declined 27% in lower canopy leaves across 35 accessions, but the decline was over twice as great in planophiles than in erectophiles. The loss of photosynthetic efficiency involved a decoupling between electron transport and assimilation. This was not associated with increased bundle sheath leakage, based on 13C measurements. In both planophiles and erectophiles, shaded leaves had greater leaf absorptivity and lower activities of key C4 enzymes than sun leaves. The erectophile form is considered more productive because it allows a more effective distribution of light through the canopy to support photosynthesis. We show that in sorghum, it provides a second benefit, maintenance of higher Φ CO2,max,app to support efficient use of that light resource.

Quantifying high-temperature stress on soybean canopy photosynthesis: The unique role of sun-induced chlorophyll fluorescence.

High temperature and accompanying high vapor pressure deficit often stress plants without causing distinctive changes in plant canopy structure and consequential spectral signatures. Sun-induced chlorophyll fluorescence (SIF), because of its mechanistic link with photosynthesis, may better detect such stress than remote sensing techniques relying on spectral reflectance signatures of canopy structural changes. However, our understanding about physiological mechanisms of SIF and its unique potential for physiological stress detection remains less clear. In this study, we measured SIF at a high-temperature experiment, Temperature Free-Air Controlled Enhancement, to explore the potential of SIF for physiological investigations. The experiment provided a gradient of soybean canopy temperature with 1.5, 3.0, 4.5, and 6.0°C above the ambient canopy temperature in the open field environments. SIF yield, which is normalized by incident radiation and the fraction of absorbed photosynthetically active radiation, showed a high correlation with photosynthetic light use efficiency (r = 0.89) and captured dynamic plant responses to high-temperature conditions. SIF yield was affected by canopy structural and plant physiological changes associated with high-temperature stress (partial correlation r = 0.60 and -0.23). Near-infrared reflectance of vegetation, only affected by canopy structural changes, was used to minimize the canopy structural impact on SIF yield and to retrieve physiological SIF yield (ΦF ) signals. ΦF further excludes the canopy structural impact than SIF yield and indicates plant physiological variability, and we found that ΦF outperformed SIF yield in responding to physiological stress (r = -0.37). Our findings highlight that ΦF sensitively responded to the physiological downregulation of soybean gross primary productivity under high temperature. ΦF , if reliably derived from satellite SIF, can support monitoring regional crop growth and different ecosystems' vegetation productivity under environmental stress and climate change.