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Information on growth of rye (Secale cereale L.) and triticale (X Triticosecale Wittmark) are scarce. In 2007/08 and 2008/09, winter rye, winter triticale and winter wheat (Triticum aestivum L.) were simultaneously grown in combination with 4 nitrogen (N) treatments (0/0, 40/40, 80/80 and 120/120 kg N ha-1) at the Hohenschulen Experimental Farm in northern Germany allowing for a comparison of the resource capture and biomass accumulation during spring growth. Several canopy traits (e.g. green area index (GAI), specific leaf area (SLA), light use efficiency (LUE)) as well as N dilution curves of the whole shoots, leaves and stems were determined. Triticale achieved highest GAI in throughout both growth periods. An extended growth period of wheat partly compensated for a lower GAI, thus the differences in the amount of intercepted photosynthetically active radiation (Q) between the crops remained small. In general, rye leaves were thinner (higher SLA) compared to wheat and triticale showing similar SLA, except at ear emergence in both years. Total above-ground dry matter accumulation at dough ripening was lowest in wheat mainly due to a lower LUE which in turn may be the result of a lower specific leaf area. The N dilution curves revealed a clear reduction of stem N concentration with increasing dry matter accumulation, whereas leaf N concentration only slightly decreased presumably in order to maintain optimal photosynthesis. The presented results enhance the understanding of the growth of rye and triticale and allow improving crop growth modeling of both crops. [ABSTRACT FROM AUTHOR]

Accurate estimation of the gross primary production (GPP) of terrestrial ecosystems is vital for a better understanding of the spatial-temporal patterns of the global carbon cycle. In this study, we estimate GPP in North America (NA) using the satellite-based Vegetation Photosynthesis Model (VPM), MODIS images at 8-day temporal and 500 m spatial resolutions, and NCEP-NARR (National Center for Environmental Prediction-North America Regional Reanalysis) climate data. The simulated GPP (GPP VPM ) agrees well with the flux tower derived GPP (GPP EC ) at 39 AmeriFlux sites (155 site-years). The GPP VPM in 2010 is spatially aggregated to 0.5 by 0.5° grid cells and then compared with sun-induced chlorophyll fluorescence (SIF) data from Global Ozone Monitoring Instrument 2 (GOME-2), which is directly related to vegetation photosynthesis. Spatial distribution and seasonal dynamics of GPP VPM and GOME-2 SIF show good consistency. At the biome scale, GPP VPM and SIF shows strong linear relationships (R 2 > 0.95) and small variations in regression slopes (4.60–5.55 g C m − 2 day − 1 /mW m − 2 nm − 1 sr − 1 ). The total annual GPP VPM in NA in 2010 is approximately 13.53 Pg C year − 1 , which accounts for ~ 11.0% of the global terrestrial GPP and is within the range of annual GPP estimates from six other process-based and data-driven models (11.35–22.23 Pg C year − 1 ). Among the seven models, some models did not capture the spatial pattern of GOME-2 SIF data at annual scale, especially in Midwest cropland region. The results from this study demonstrate the reliable performance of VPM at the continental scale, and the potential of SIF data being used as a benchmark to compare with GPP models. [ABSTRACT FROM AUTHOR]

Peatland net ecosystem production is a key variable to assess changes in the functional role of peatlands in the global carbon cycle. Light use efficiency (LUE) models in combination with satellite data have been used to estimate production for most major ecosystems, but peatlands have been largely ignored. The objectives of this study were: 1) to examine how the LUE parameter epsilon, ε (the amount of carbon fixed or converted to biomass per unit absorbed photosynthetically active radiation), varies between and within four different peatlands; 2) to examine how the variations in ε relate to variations in environmental conditions; and 3) to evaluate a LUE-based model for estimation of ε in peatlands. We achieve these objectives using a combination of eddy covariance flux measurements, climate data and satellite data and estimate ε using the LUE-based vegetation photosynthesis model (VPM). The results show that: 1) mean site-specific flux-derived ε values (± standard deviation) were split into three statistically different groups: lowest values at the two colder fens, Kaamanen and Sandhill (0.22 ± 0.18 and 0.23 ± 0.20 g C MJ − 1 , respectively), highest values at the treed fen La Biche (0.47 ± 0.27 g C MJ − 1 ) and intermediate values at the bog, Mer Bleue (0.34 ± 0.18 g C MJ − 1 ); 2) Variations in monthly ε within sites related mainly to air temperature, while variations in annual ε within sites related mainly to wetness variables; 3) relative mean absolute errors of estimates of ε for the four sites ranged between 19% and 35%, with r 2 values ranging between 72% and 93%. LUE models are appealing as they are relatively simple formulations of variables that are easily obtained from satellite data. Challenges associated with the use of satellite data derived input variables are further discussed in the paper. [ABSTRACT FROM AUTHOR]

Modeling crop gross primary production (GPP) is critical to understanding the carbon dynamics of agro-ecosystems. Satellite-based studies have widely used production efficiency models (PEM) to estimate cropland GPP, wherein light use efficiency (LUE) is a key model parameter. One factor that has not been well considered in many PEMs is that canopy LUE could vary with illumination conditions. This study investigates how the partitioning of diffuse and direct solar radiation influences cropland GPP using both flux tower and satellite data. The field-measured hourly LUE under cloudy conditions was 1.50 and 1.70 times higher than that under near clear-sky conditions for irrigated corn and soybean, respectively. We applied a two-leaf model to simulate the canopy radiative transfer process, where modeled photosynthetically active radiation (PAR) absorbed by canopy agreed with tower measurements (R 2 = 0.959 and 0.914 for corn and soybean, respectively). Derived canopy LUE became similar after accounting for the impact of light saturation on leaf photosynthetic capacity under varied illumination conditions. The impacts of solar radiation partitioning on satellite-based modeling of crop GPP was examined using vegetation indices (VI) derived from MODIS data. Consistent with the field modeling results, the relationship between daily GPP and PAR × VI under varied illumination conditions showed different patterns in terms of regression slope and intercept. We proposed a function to correct the influences of direct and diffuse radiation partitioning and the explained variance of flux tower GPP increased in all experiments. Our results suggest that the non-linear response of leaf photosynthesis to light absorption contributes to higher canopy LUE on cloudy days than on clear days. We conclude that accounting for the impacts of solar radiation partitioning is necessary for modeling crop GPP on a daily or shorter basis. [ABSTRACT FROM AUTHOR]

Light wavelengths preferentially absorbed by chlorophyll (chl) often display steep absorption gradients. This oversaturates photosynthesis in upper chloroplasts and deprives lower chloroplasts of blue and red light. Reducing chl content could create a more even leaf light distribution and thereby increase leaf light-use efficiency and overall canopy photosynthesis. This was tested on soybean cultivar 'Clark' (WT) and a near-isogenic chl b deficient mutant, Y11y11, grown in controlled environment chambers and in the field. Light attenuation was quantified using a novel approach involving light sheet microscopy. Leaf adaxial and abaxial surfaces were illuminated separately with blue, red, and green wavelengths, and chl fluorescence was detected orthogonally to the illumination plane. Relative fluorescence was significantly greater in deeper layers of the Y11y11 mesophyll than in WT, with the greatest differences in blue, then red, and finally green light when illuminated from the adaxial surface. Modeled relative photosynthesis based on chlorophyll profiles and Beer's Law predicted less steep gradients in mutant relative photosynthesis rates compared to WT. Although photosynthetic light-use efficiency was greater in the field-grown mutant with ~50% lower chl, light-use efficiency was lower in the mutant when grown in chambers where chl was ~80% reduced. This difference is probably due to pleiotropic effects of the mutation that accompany very severe reductions in chlorophyll and may warrant further testing in other low-chl lines. [ABSTRACT FROM AUTHOR]

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Light use efficiency (LUE) plays a vital role in determination of crop biomass and yield. Important components of LUE, i.e. canopy structure, nitrogen distribution, photosynthetic capacity and CO 2 diffusion conductance were investigated in paddy rice grown under low, normal and high supplemental nitrogen (0, 115, and 180 kg N ha −1 ). Photosynthetic characteristics varied linearly with leaf nitrogen content (N a ), independent of treatment and canopy position, so differences in photosynthesis were due to differences in N allocation. CO 2 diffusion resistances were significant and constrained LUE (more during late season), but there were no differences between treatments. Early in the season (tillering stage) leaves in the fertilized treatments had higher photosynthetic rates due to higher leaf N content leading to larger amounts of rate-limiting photosynthetic proteins, which gave them an early head start and boost in productivity and leaf area index (LAI), bringing increases in canopy light interception. Later during the growth season, differences in leaf N a and photosynthetic characteristics between treatments were slight. Enhanced LAI in fertilized plots throughout the growing season was related to greater leaf number and leaf area per planted bundle and a larger leaf area in the upper canopy (LAUC). Fertilized treatments had a higher LAUC with high leaf nitrogen concentration and reduced mesophyll diffusion limitation but greater exposure to full sunlight that led to improved nitrogen use efficiency and efficient carbon gain. In conclusion, differences in carbon gain and biomass accumulation under differing N fertilization were associated primarily with resource allocation associated with canopy leaf area development rather than leaf morphological or physiological properties. The results provide new insights with respect to the multidimensional coordinated structural and physiological adjustments governing LUE over the course of rice crop development. [ABSTRACT FROM AUTHOR]

We investigated the use of a simplified soilless tomato culture system fertilized with controlled-release fertilizers (CRF) in northern Japan. The utility of this system was assessed by measuring plant growth, fruit yield, yield components, light interception by plant canopy and nutrient use during August–January. Five levels of fertilizer (5–15 g N per plant) were applied using CRF in a closed system and these were compared with plants given liquid fertilizer (LF) with an electrical conductivity (EC) control in an open system. The CRF was supplied in a water recirculation tank. Total N supply in the LF system (19.7 g N per plant) was higher than in the CRF treatments. In the latter, fresh fruit yield increased with higher fertilizer levels up to 12.5 g N. The fruit yields at 12.5 and 15.0 g N were similar to those with the LF system. The high fresh fruit yield was caused by high dry matter yield regardless of the CRF treatments, which showed similar dry matter distribution to fruit. A high correlation was noted between cumulative light interception and total dry matter production. Although both were high, dry matter distribution to fruit was lower in the LF system than in the CRF treatments. Overall, however, these effects resulted in similar dry and fresh fruit yields between CRF and LF treatments, with excessive vegetative growth in the LF system. We conclude that the closed CRF application system reduced 37% of nutrient use but still gave a fruit yield equivalent to that obtained by culturing with the open LF system and EC-based management. [ABSTRACT FROM AUTHOR]

Maize is one of the most important crops cultivated on the global scale. Accurate estimation of maize Gross Primary Production (GPP) can provide valuable information for regional and global carbon budget studies. From site level to regional/global scales, GPP estimation depends on remote sensing or eddy covariance flux data. In this research, the 8-day composite GPP of maize was estimated by Moderate Resolution Imaging Spectroradiometer (MODIS) and flux tower data at eight study sites using a Regional Production Efficiency Model (REG-PEM). The performance of the model was assessed by analyzing the linearly regression of GPP estimated from the REG-PEM model (GPPEST) with the GPP predicted from the eddy covariance data (GPPEC). The coefficient of determination, root mean squared error and mean absolute error of the regression model were calculated. The uncertainties of the model are also discussed in this research. The seasonal dynamics (phases and magnitudes) of the GPPEST reasonably agreed with those of GPPEC, indicating the potential of the satellite-driven REG-PEM model for up-scaling the GPP in maize croplands. Furthermore, the maize GPP estimated by this model is more accurate than the MODIS GPP products (MOD17A2). In particular, MOD17A2 significantly underestimated the GPP of maize croplands. The uncertainties in the REG-PEM model are mostly contributed by the maximum light use efficiency and the fraction of photosynthetically active radiation. [ABSTRACT FROM AUTHOR]

The prediction of the allocation of carbon and nitrogen into the different organs, e.g., leaves, stems, roots of a growing plant is a pivotal part of mechanistic growth models. Based on 2 year (winter wheat) and 3 year (winter barley) field trials with different N treatments (0–240 kg N ha −1 ), the objectives of this study were to verify currently used parameters for (i) specific leaf area, and (ii) allometric relationships between leaf and stem dry matter (DM). Green area index (GAI) differed between years and increased with N supply rate. The course of the specific leaf area (SLA) within the growth period after winter showed a large variation between the years as well as between the N treatments. During stem elongation, SLA positively correlated with GAI in both crops, indicating that leaves became thinner when the canopy was denser. The allometric relationships between stem and leaf dry DM clearly varied with the growth stages. Stem DM of wheat showed a higher proportion of the total above-ground biomass compared to barley. The effect of N fertilization on allometry remained small during early growth stages in barley, whereas in wheat, an increased N supply significantly decreased the proportion of stem DM. Our results suggest similar GAI and SLA patterns for wheat and barley, albeit depending on the N supply. Especially, using a constant SLA in plant growth models seems to be not suitable. [ABSTRACT FROM AUTHOR]

Satellite-based techniques that provide temporally and spatially continuous information over vegetated surfaces have become increasingly important in monitoring the global agriculture yield. In this study, we examine the performance of a light use efficiency model (EC-LUE) for simulating the gross primary production (GPP) and yield of crops. The EC-LUE model can explain on average approximately 90% of the variability in GPP for 36 FLUXNET sites globally. The results indicate that a universal set of parameters, independent of crop species (except for C4 crops), can be adopted in the EC-LUE model for simulating crops’ GPP. At both irrigated and rainfed sites, the EC-LUE model exhibits a similar level of performance. However, large errors are found when simulating yield based on crop harvest index. This analysis highlights the need to improve the representation of the harvest index and carbon allocation for improving crop yield estimations from satellite-based methods. [ABSTRACT FROM AUTHOR]

In 1994, LINTUL-POTATO was published, a comprehensive model of potato development and growth. The mechanistic model simulated early crop processes (emergence and leaf expansion) and light interception until extinction, through leaf layers. Photosynthesis and respiration in a previous crop growth model-SUCROS-were substituted by a temperature-dependent light use efficiency. Leaf senescence at initial crop stages was simulated by allowing a longevity per daily leaf class formed, and crop senescence started when all daily dry matter production was allocated to the tubers, leaving none for the foliage. The model performed well in, e.g., ideotyping studies. For other studies such as benchmarking production environments, agro-ecological zoning, climatic hazards, climate change, and yield gap analysis, the need was felt to develop from the original LINTUL-POTATO, a derivative LINTUL-POTATO-DSS with fewer equations-reducing the potential sources of error in calculations-and fewer parameters. This reduces the number of input parameters as well as the amount of data required that for many reasons are not available or not reliable. In LINTUL-POTATO-DSS calculating potential yields, initial crop development depends on a fixed temperature sum for ground cover development from 0% at emergence to 100%. Light use efficiency is temperature dependent. Dry matter distribution to the tubers starts at tuber initiation and linearly increases up to a fixed harvest index which is reached at crop end. Crop end is input of the model: it is assumed that the crop cycle determined by maturity matches the length of the available frost-free and or heat-free cropping season. LINTUL-POTATO-DSS includes novel calculations to explore tuber quality characteristics such as tuber size distribution and dry matter concentration depending on crop environment and management. [ABSTRACT FROM AUTHOR]

Understanding plant photosynthesis, or Gross Primary Production (GPP), is a crucial aspect of quantifying the terrestrial carbon cycle. Remote sensing approaches, in particular multi-angular spectroscopy, have proven successful for studying relationships between canopy-reflectance and plant-physiology processes, thus providing a mechanism to scale up. However, many different instrumentation designs exist and few cross-comparisons have been undertaken. This paper discusses the design evolution of the Automated Multiangular SPectro-radiometer for Estimation of Canopy reflectance (AMSPEC) series of instruments. Specifically, we assess the performance of the PP-Systems Unispec-DC and Ocean Optics JAZ-COMBO spectro-radiometers installed on an updated, tower-based AMSPEC-III system. We demonstrate the interoperability of these spectro-radiometers, and the results obtained suggest that JAZ-COMBO can successfully be used to substitute more expensive measurement units for detecting and investigating photosynthesis and canopy spectra. We demonstrate close correlations between JAZ-COMBO and Unispec-DC measured canopy radiance (0.75 ≤ R² ≤ 0.85) and solar irradiance (0.95 ≤ R² ≤ 0.96) over a three month time span. We also demonstrate close agreement between the bi-directional distribution functions obtained from each instrument. We conclude that cost effective alternatives may allow a network of AMSPEC-III systems to simultaneously monitor various vegetation types in different ecosystems. This will allow to scale and improve our understanding of the interactions between vegetation physiology and spectral characteristics, calibrate broad-scale observations to stand-level measurements, and ultimately lead to improved understanding of changing vegetation spectral features from satellite. [ABSTRACT FROM AUTHOR]

Vegetation productivity metrics, such as gross primary production (GPP) may be determined from the efficiency with which light is converted into photosynthates, or light use efficiency ( ϵ ). Therefore, accurate measurements and modeling of ϵ is important for estimating GPP in each ecosystem. Previous studies have quantified the impacts of biophysical parameters on light use efficiency based GPP models. Here we enhance previous models utilizing four scalars for light quality (i.e., cloudiness), temperature, water stress, and phenology for data collected from both maize and soybean crops at three Nebraska AmeriFlux sites between 2001 and 2012 (maize: 26 field-years; soybean: 10 field-years). The cloudiness scalar was based on the ratio of incident photosynthetically active radiation (PAR in ) to potential (i.e., clear sky) PAR pot . The water stress and phenology scalars were based on vapor pressure deficit and green leaf area index, respectively. Our analysis determined that each parameter significantly improved the estimation of GPP (AIC range: 2503–2740; likelihood ratio test: p -value < 0.0003, df = 5–8). Daily GPP data from 2001 to 2008 calibrated the coefficients for the model with reasonable amount of error and bias (RMSE = 2.2 g C m −2 d −1 ; MNB = 4.7%). Daily GPP data from 2009 to 2012 tested the model with similar accuracy (RMSE = 2.6 g C m −2 d −1 ; MNB = 1.7%). Modeled GPP was generally within 10% of measured growing season totals in each year from 2009 to 2012. Cumulatively, over the same four years, the sum of error and the sum of absolute error between the measured and modeled GPP, which provide measures of long-term bias, was ±5% and 2–9%, respectively, among the three sites. [ABSTRACT FROM AUTHOR]

Spatio-temporal mismatches between Remote Sensing (RS) and Eddy Covariance (EC) data as well as spatial heterogeneity jeopardize terrestrial Gross Primary Production (GPP) modeling. This article combines: (a) high spatial resolution hyperspectral imagery; (b) EC footprint climatology estimates; and (c) semi-empirical models of increasing complexity to analyze the impact of these factors on GPP estimation. Analyses are carried out in a Mediterranean Tree-Grass Ecosystem (TGE) that combines vegetation with very different physiologies and structure. Half-hourly GPP (GPPhh) were predicted with relative errors ~36%. Results suggest that, at EC footprint scale, the ecosystem signals are quite homogeneous, despite tree and grass mixture. Models fit using EC and RS data with high degree of spatial and temporal match did not significantly improved models performance; in fact, errors were explained by meteorological variables instead. In addition, the performance of the different models was quite similar. This suggests that none of the models accurately represented light use efficiency or the fraction of absorbed photosynthetically active radiation. This is partly due to model formulation; however, results also suggest that the mixture of the different vegetation types might contribute to hamper such modeling, and should be accounted for GPP models in TGE and other heterogeneous ecosystems.

Although quantifying the massive exchange of carbon that takes place over the Amazon Basin remains a challenge, progress is being made as the remote sensing community moves from using traditional, reflectance-based vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), to the more functional Photochemical Reflectance Index (PRI). This new index, together with satellite-derived estimates of canopy light interception and Sun-Induced Fluorescence (SIF), provide improved estimates of Gross Primary Production (GPP). This paper traces the development of these new approaches, compares the results of their analyses from multiple years of data acquired across the Amazon Basin and suggests further improvements in instrument design, data acquisition and processing. We demonstrated that our estimates of PRI are in generally good agreement with eddy-flux tower measurements of photosynthetic light use efficiency (ε) at four sites in the Amazon Basin: r2 values ranged from 0.37 to 0.51 for northern flux sites and to 0.78 for southern flux sites. This is a significant advance over previous approaches seeking to establish a link between global-scale photosynthetic activity and remotely-sensed data. When combined with measurements of Sun-Induced Fluorescence (SIF), PRI provides realistic estimates of seasonal variation in photosynthesis over the Amazon that relate well to the wet and dry seasons. We anticipate that our findings will steer the development of improved approaches to estimate photosynthetic activity over the tropics.

Crop growth and development, as well as ecophysiological assessments, can be obtained directly from field experiments, but only a finite set of variables can be studied in this way. A model used to simulate the biological efficiency cycle considering the soil-plant-climate system is essential in assessing the associated fluctuations due to year-to-year variability of the climate, or for testing the effect of various cropping practices. Models that simulate crop growth were developed, on one hand, to predict the yields, and on the other hand to analyze the impacts of agricultural practices on environment and productivity. The aims of this study were to develop a crop simulation model, coupled with experimental work on phenotypic and physiological mechanisms and their interactions in binary associations of perennial legumes and grasses. A logical flow diagram to conceive and interact for prototyping was built. Experimental data comprising crop growth values required for simulation were implemented as variables in each specific module. The diagrammatic model helped to assess the components of the Lab VIEW software application as state variables, auxiliary variables, processes and parameters. At individual level, interspecific competition capacity depends on the deployment efficiency of morphological organs specialized to gain resources. Crop models could use the added structural detail by incorporating detailed routines of light interception and resource allocation to respond to spatial variations of local conditions that result from species growth within mixed canopy. [ABSTRACT FROM AUTHOR]

Population growth and globally increasing standards of living have put a significant strain on the energy-food-water nexus. Limited water availability particularly affects agriculture, as it accounts for over 70% of global freshwater withdrawals (Aquastat). This study outlines the fundamental nature of plant water consumption and suggests a >50% reduction in renewable freshwater demand is possible by engineering more reflective crops. Furthermore, the decreased radiative forcing resulting from the greater reflectivity of crops would be equivalent to removing 10-50 ppm CO2 from the atmosphere. Recent advances in engineering optical devices and a greater understanding of the mechanisms of biological reflectance suggest such a strategy may now be viable. Here we outline the challenges involved in such an effort and suggest three potential approaches that could enable its implementation. While the local benefits may be straightforward, determining the global externalities will require careful modelling efforts and gradually scaled field trials. [ABSTRACT FROM AUTHOR]

In this study, the evidence of a relationship between the physiological state and leaf optical properties led us to investigate the behaviour of two species – Betula pendula Roth and Carpinus betulus L. – in order to determine if the vegetation is affected by the heavy metal load of the soil from a former mining area, Rosia Montana, Romania (46°18′47″N, 23°10′15″E). Two non-destructive techniques (leaf transmittance and reflectance) were compared to assess the physiological state of the plants, and 27 vegetation indices for estimating foliar pigment content were tested. For chlorophyll assessment, the reflectance ratios R NIR / R 550 and R NIR / R 700 were the most reliable for an accurate estimation of the pigment content of the leaves. The light use efficiency (LUE) indices were within normal values, except the Red Green Ratio, indicating that the overall reactions of photosynthesis are not impaired by the specific conditions of the habitat. Differences in leaf optical properties are minimal between the two studied species and the values of the vegetation indices are showing similar trends. Both techniques performed consistently well and yielded accurate estimations of pigment content, supporting their interoperability. [ABSTRACT FROM AUTHOR]

Accurate estimates of gross primary production (GPP) for croplands are needed to assess carbon cycle and crop yield. Satellite-based models have been developed to monitor spatial and temporal GPP patterns. However, there are still large uncertainties in estimating cropland GPP. This study compares three light use efficiency (LUE) models (MODIS-GPP, EC-LUE, and VPM) with eddy-covariance measurements at three adjacent AmeriFlux crop sites located near Mead, Nebraska, USA. These sites have different crop-rotation systems (continuous maize vs. maize and soybean rotated annually) and water management practices (irrigation vs. rainfed). The results reveal several major uncertainties in estimating GPP which need to be sufficiently considered in future model improvements. Firstly, the C4 crop species (maize) shows a larger photosynthetic capacity compared to the C3 species (soybean). LUE models need to use different model parameters (i.e., maximal light use efficiency) for C3 and C4 crop species, and thus, it is necessary to have accurate species-distribution products in order to determine regional and global estimates of GPP. Secondly, the 1 km sized MODIS fPAR and EVI products, which are used to remotely identify the fraction of photosynthetically active radiation absorbed by the vegetation canopy, may not accurately reflect differences in phenology between maize and soybean. Such errors will propagate in the GPP model, reducing estimation accuracy. Thirdly, the water-stress variables in the remote sensing models do not fully characterize the impacts of water availability on vegetation production. This analysis highlights the need to improve LUE models with regard to model parameters, vegetation indices, and water-stress inputs. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to study land–atmosphere exchange of carbon dioxide (CO 2 ) for semi-arid savanna ecosystems of the Sahel region and its response to climatic and environmental change. A subsidiary aim is to study and quantify the seasonal dynamics in light use efficiency ( ϵ ) being a key variable in scaling carbon fluxes from ground observations using earth observation data. The net ecosystem exchange of carbon dioxide (NEE) 2010–2013 was measured using the eddy covariance technique at a grazed semi-arid savanna site in Senegal, West Africa. Night-time NEE was not related to temperature, confirming that care should be taken before applying temperature response curves for hot dry semi-arid regions when partitioning NEE into gross primary productivity (GPP) and ecosystem respiration ( R eco ). Partitioning was instead done using light response curves. The values of ϵ ranged between 0.02 g carbon (C) MJ −1 for the dry season and 2.27 g C MJ −1 for the peak of the rainy season, and its seasonal dynamics was governed by vegetation phenology, photosynthetically active radiation, soil moisture and vapor pressure deficit (VPD). The CO 2 exchange fluxes were very high in comparison to other semi-arid savanna sites; half-hourly GPP and R eco peaked at −43 μmol CO 2 m −2 s −1 and 20 μmol CO 2 m −2 s −1 , and daily GPP and R eco peaked at −15 g C m −2 and 12 g C m −2 , respectively. Possible explanations for the high CO 2 fluxes are a high fraction of C4 species, alleviated water stress conditions, and a strong grazing pressure that results in compensatory growth and fertilization effects. We also conclude that vegetation phenology, soil moisture, radiation, VPD and temperature were major components in determining the seasonal dynamics of CO 2 fluxes. Despite the height of the peak of the growing season CO 2 fluxes, the annual C budget (average NEE: −271 g C m −2 ) were similar to that in other semi-arid ecosystems because the short rainy season resulted in a short growing season. Global circulation models project a decrease in rainfall, an increase in temperature and a shorter growing season for the western Sahel region, and the productivity and the sink function of this semi-arid ecosystem may thus be lower in the future. [ABSTRACT FROM AUTHOR]

The Photochemical Reflectance Index (PRI) of green leaves is an indicator of photosynthetic downregulation: when the photosynthetic apparatus is close to the saturation limit, PRI becomes dependent on light conditions. Therefore, by measuring the PRI of leaves under different local irradiance conditions, it should be possible to determine the saturation level of the leaves and obtain information on the light use efficiency (LUE) of a vegetation canopy. The dependence of PRI on the ratio of sunlit to shaded foliage (quantified by the canopy shadow fraction) in the field of view of an instrument has been used to remotely measure canopy LUE on clear days. However, besides photosynthetic downregulation, the dependence of canopy PRI on shadow fraction is affected by the blue sky radiation caused by scattering in the atmosphere. To quantify this effect on remotely sensed PRI, we present the underlying definitions relating leaf and canopy PRI and perform the required calculations for typical midsummer conditions in Central Finland. We demonstrate that the effect of blue sky radiation on the variation of PRI with canopy shadow fraction is similar in shape and magnitude to that of LUE variations reported in literature. [ABSTRACT FROM AUTHOR]

Climate and physiological controls of vegetation gross primary production (GPP) vary in space and time. In many ecosystems, GPP is primary limited by absorbed photosynthetically-active radiation; in others by canopy conductance. These controls further vary in importance over daily to seasonal time scales. We propose a simple but effective conceptual model that estimates GPP as the lesser of a conductance-limited ( F c ) and radiation-limited ( Fr ) assimilation rate. F c is estimated from canopy conductance while Fr is estimated using a light use efficiency model. Both can be related to vegetation properties observed by optical remote sensing. The model has only two fitting parameters: maximum light use efficiency, and the minimum achieved ratio of internal to external CO 2 concentration. The two parameters were estimated using data from 16 eddy covariance flux towers for six major biomes including both energy- and water-limited ecosystems. Evaluation of model estimates with flux tower-derived GPP compared favourably to that of more complex models, for fluxes averaged; per day (r 2 = 0.72, root mean square error, RMSE = 2.48 μmol C m 2 s − 1 , relative percentage error, RPE = − 11%), over 8-day periods (r 2 = 0.78 RMSE = 2.09 μmol C m 2 s − 1 ,RPE = − 10%), over months (r 2 = 0.79, RMSE = 1.93 μmol C m 2 s − 1 , RPE = − 9%) and over years (r 2 = 0.54, RMSE = 1.62 μmol C m 2 s − 1 , RPE = − 9%). Using the model we estimated global GPP of 107 Pg C y − 1 for 2000–2011. This value is within the range reported by other GPP models and the spatial and inter-annual patterns compared favourably. The main advantages of the proposed model are its simplicity, avoiding the use of uncertain biome- or land-cover class mapping, and inclusion of explicit coupling between GPP and plant transpiration. [ABSTRACT FROM AUTHOR]

To examine the effect of nitrogen (N) fertilization on forest carbon (C) sequestration and water loss, we used an artificial neural network model to estimate C fluxes and evapotranspiration (ET) in response to N fertilization during four post-fertilization years in a Pacific Northwest chronosequence of three Douglas-fir stands aged 61, 22 and 10 years old in 2010 (DF49, HDF88 and HDF00, respectively). Results showed that N fertilization increased gross primary productivity (GPP) for all three sites in all four years with the largest absolute increase at HDF00 followed by HDF88. Ecosystem respiration increased in all four years at HDF00, but decreased over the last three years at HDF88 and over all four years at DF49. As a result, fertilization increased the net ecosystem productivity of all three stands with the largest increase at HDF88, followed by DF49. Fertilization had no discernible effect on ET in any of the stands. Consequently, fertilization increased water use efficiency (WUE) in all four post-fertilization years at all three sites and also increased light use efficiency (LUE) of all the stands, especially HDF00. Our results suggest that the effects of fertilization on forest C sequestration and water loss may be associated with stand age and fertilization; the two younger stands appeared to be more efficient than the older stand with respect to GPP, WUE and LUE. [ABSTRACT FROM AUTHOR]

The objectives of this study were to develop a growth model of pak-choi using an expolinear function and to estimate the effect of the radiation integral and planting density on the crop growth rate. The growth and yield of hydroponically grown pak-choi (B rassica campestris ssp. chinensis) were investigated at four different planting densities (27, 33, 44, and 67 plants/m). The shoot dry and fresh weights per m increased with increasing planting density and could be expressed as expolinear functions based on the daily photosynthetically active radiation (PAR). A linear relationship was observed between the shoot dry weight and fresh weight, regardless of the planting density. The maximum crop growth rate and light use efficiency (LUE) increased with increasing planting density, but the lost daily PAR integral decreased. The maximum relative growth rate was not significantly different among the planting densities. Using the relative growth rate per the daily PAR integral and LUE, an expolinear growth model of pak-choi was developed. The measured and estimated shoot dry weights exhibited strong agreement with Y = 1.010·X (R = 0.982***) using the PAR and leaf area index as inputs. The expolinear growth model was determined to be useful for quantifying the growth and yield of pak-choi in controlled environments. [ABSTRACT FROM AUTHOR]

An accurate estimation of carbon fluxes is very important in carbon cycle studies. A remote sensing based gross primary production (GPP) and net ecosystem production (NEP) algorithm, RS-CFLUX, was presented in this work. The algorithm was calibrated with Markov Chain Monte Carlo (MCMC) method at Daman superstation and Zhangye wetland station in the midstream of the Heihe River Basin. Results indicated that both of the stations present high GPP (1442.04 g C/m2/year at Daman superstation and 928.89 g C/m2/year at Zhangye wetland station) and NEP (409.38 g C/m2/year at Daman superstation and 422.60 g C/m2/year at Zhangye wetland station). The RS-CFLUX model can correctly simulate the seasonal dynamics and quantities of carbon fluxes at both stations, using photosynthetically active radiation (PAR), land surface temperature (LST), normalized difference water index (NDWI) and enhanced vegetation index (EVI) as input. RS-CFLUX model were sensitive to maximum light use efficiency, respiration at reference temperature, activation energy parameter of respiration. [ABSTRACT FROM AUTHOR]

Vegetation productivity metrics such as gross primary production (GPP) at the canopy scale are greatly affected by the efficiency of using absorbed radiation for photosynthesis, or light use efficiency (LUE). Thus, close investigation of the relationships between canopy GPP and photosynthetically active radiation absorbed by vegetation is the basis for quantification of LUE. We used multiyear observations over irrigated and rainfed contrasting C3 (soybean) and C4 (maize) crops having different physiology, leaf structure, and canopy architecture to establish the relationships between canopy GPP and radiation absorbed by vegetation and quantify LUE. Although multiple LUE definitions are reported in the literature, we used a definition of efficiency of light use by photosynthetically active “green” vegetation (LUE green ) based on radiation absorbed by “green” photosynthetically active vegetation on a daily basis. We quantified, irreversible slowly changing seasonal (constitutive) and rapidly day-to-day changing (facultative) LUE green , as well as sensitivity of LUE green to the magnitude of incident radiation and drought events. Large (2–3-fold) variation of daily LUE green over the course of a growing season that is governed by crop physiological and phenological status was observed. The day-to-day variations of LUE green oscillated with magnitude 10–15% around the seasonal LUE green trend and appeared to be closely related to day-to-day variations of magnitude and composition of incident radiation. Our results show the high variability of LUE green between C3 and C4 crop species (1.43 gC/MJ vs. 2.24 gC/MJ, respectively), as well as within single crop species (i.e., maize or soybean). This implies that assuming LUE green as a constant value in GPP models is not warranted for the crops studied, and brings unpredictable uncertainties of remote GPP estimation, which should be accounted for in LUE models. The uncertainty of GPP estimation due to facultative and constitutive changes in LUE green can be considered as a critical component of the total error budget in the context of remotely sensed based estimations of GPP. The quantitative framework of LUE green estimation presented here offers a way of characterizing LUE green in plants that can be used to assess their phenological and physiological status and vulnerability to drought under current and future climatic conditions and is essential for calibration and validation of globally applied LUE algorithms. [ABSTRACT FROM AUTHOR]

Accurate simulation of terrestrial gross primary production (GPP), the largest global carbon flux, benefits our understanding of carbon cycle and its source of variation. This paper presents a novel light use efficiency-based GPP model called the terrestrial ecosystem carbon flux model (TEC) driven by MODIS FPAR and climate data coupled with a precipitation-driven evapotranspiration (E) model ( Yan et al., 2012 ). TEC incorporated a new water stress factor, defined as the ratio of actual E to Priestley and Taylor (1972) potential evaporation (EPT). A maximum light use efficiency (ϵ*) of 1.8 gC MJ−1 and 2.76 gC MJ−1 was applied to C3 and C4 ecosystems, respectively. An evaluation at 18 eddy covariance flux towers representing various ecosystem types under various climates indicates that the TEC model predicted monthly average GPP for all sites with overall statistics of r = 0.85, RMSE = 2.20 gC m−2 day−1, and bias = −0.05 gC m−2 day−1. For comparison the MODIS GPP products (MOD17A2) had overall statistics of r = 0.73, RMSE = 2.82 gC m−2 day−1, and bias = −0.31 gC m−2 day−1 for this same set of data. In this case, the TEC model performed better than MOD17A2 products, especially for C4 plants. We obtained an estimate of global mean annual GPP flux at 128.2 ± 1.5 Pg C yr −1 from monthly MODIS FPAR and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA reanalysis data at a 1.0° spatial resolution over 11 year period from 2000 to 2010. This falls in the range of published land GPP estimates that consider the effect of C4 and C3 species. The TEC model with its new definition of water stress factor and its parameterization of C4 and C3 plants should help better understand the coupled climate-carbon cycle processes. [ABSTRACT FROM AUTHOR]

The Moderate Resolution Imaging Spectroradiometer (MODIS) yields global operational estimates of terrestrial gross primary production (GPP). In this study, we compared MOD17A2 GPP with tower eddy flux-based estimates of GPP from 2001 to 2010 over an evergreen broad-leaf Mediterranean forest in Southern France with a significant summer drought period. The MOD17A2 GPP shows seasonal variations that are inconsistent with the tower GPP, with close-to-accurate winter estimates and significant discrepancies for summer estimates which are the least accurate. The analysis indicated that the MOD17A2 GPP has high bias relative to tower GPP during severe summer drought which we hypothesized caused by soil water limitation. Our investigation showed that there was a significant correlation (R2 = 0.77, p < 0.0001) between the relative soil water content and the relative error of MOD17A2 GPP. Therefore, the relationship between the error and the measured relative soil water content could explain anomalies in MOD17A2 GPP. The results of this study indicate that careful consideration of the water conditions input to the MOD17A2 GPP algorithm on remote sensing is required in order to provide accurate predictions of GPP. Still, continued efforts are necessary to ascertain the most appropriate index, which characterizes soil water limitation in water-limited environments using remote sensing. [ABSTRACT FROM AUTHOR]

A primary focus of this short communication is to show how the operational definition of light use efficiency (LUE) influences the results and interpretation of the LUE model. Our study was motivated by the observation that multiple LUE definitions are reported in the literature. The temporal behavior of three operational definitions of LUE, based on (i) incident radiation, (ii) total absorbed radiation and (iii) radiation absorbed by photosynthetically active/green vegetation was examined for two contrasting crops (soybean and maize) having different physiologies, leaf structures and canopy architectures. Over the course of a growing season, the behavior of these three contrasting LUE definitions was strikingly dissimilar, and the degree of dissimilarity varied with contrasting crops (corn and soybean). This demonstrates that LUE model behavior would vary strongly with the LUE definition used, with resulting implications both for the estimated seasonal productivity, and for the interpretation of the underlying mechanism. Based on these findings, we recommend a standard definition of the LUE model based on radiation absorbed by green vegetation. We also discuss the practical and theoretical implications of using this simple conceptual model on a dynamic biological system. [ABSTRACT FROM AUTHOR]

Gross primary production (GPP) of croplands may be used to quantify crop productivity and evaluate a range of management practices. Eddy flux data from three soybean ( Glycine max L.) fields under different management practices (no-till vs. till; rainfed vs. irrigated) and Moderate Resolution Imaging Spectroradiometer (MODIS) derived vegetation indices (VIs) were used to test the capabilities of remotely sensed VIs and soybean phenology to estimate the seasonal dynamics of carbon fluxes. The modeled GPP (GPP VPM ) using vegetation photosynthesis model (VPM) was compared with the GPP (GPP EC ) estimated from eddy covariance measurements. The VIs tracked soybean phenology well and delineated the growing season length (GSL), which was closely related to carbon uptake period (CUP, R 2 = 0.84), seasonal sums of net ecosystem CO 2 exchange (NEE, R 2 = 0.78), and GPP EC ( R 2 = 0.54). Land surface water index (LSWI) tracked drought-impacted vegetation well, as the LSWI values were positive during non-drought periods and negative during severe droughts within the soybean growing season. On a seasonal scale, NEE of the soybean sites ranged from −37 to −264 g C m −2 . The result suggests that rainfed soybean fields needed about 450–500 mm of well-distributed seasonal rainfall to maximize the net carbon sink. During non-drought conditions, VPM accurately estimated seasonal dynamics and interannual variation of GPP of soybean under different management practices. However, some large discrepancies between GPP VPM and GPP EC were observed under drought conditions as the VI did not reflect the corresponding decrease in GPP EC . Diurnal GPP EC dynamics showed a bimodal distribution with a pronounced midday depression at the period of higher water vapor pressure deficit (>1.2 kPa). A modified W scalar based on LSWI to account for the water stress in VPM helped quantify the reduction in GPP during severe drought and the model’s performance improved substantially. In conclusion, this study demonstrates the potential of integrating vegetation activity through satellite remote sensing with ground-based flux and climate data for a better understanding and upscaling of carbon fluxes of soybean croplands. [ABSTRACT FROM AUTHOR]

sCASE is an automated continuous monitoring system that produces near real-time daily gross primary productivity (GPP) estimates of North Pindus National Park forests. The algorithm is based on the light use efficiency (ε) approach, incorporating species-specific maximum ε values (εo) and εo downscaling according to temperature, water scarcity and leaf developmental stage. sCASE was calibrated using a series of 2-year field ecophysiological measurements on the 4 dominant forest species of the Park and validated against a canopy photosynthesis model (R² = 0.9, RMSE = 1.29 g C m-2 day-1). sCASE offers a comprehensive view of the spatial distribution and the temporal progress of GPP and other products (e.g. LAI, FAPAR, ETo) through a freely accessible online GIS platform. [ABSTRACT FROM AUTHOR]

Models of gross primary production (GPP) based on remote sensing measurements are currently parameterized with vegetation-specific parameter sets and therefore require accurate information on the distribution of vegetation to drive them. Can this parameterization scheme be replaced with a vegetation-invariant set of parameters that can maintain or increase model applicability by reducing errors introduced from the uncertainty of land cover classification? Based on the measurements of ecosystem carbon fluxes from 168 globally distributed sites in a range of vegetation types, we examined the predictive capacity of seven light use efficiency (LUE) models. Two model experiments were conducted: (i) a constant set of parameters for various vegetation types and (ii) vegetation-specific parameters. The results showed no significant differences in model performance in simulating GPP while using both set of parameters. These results indicate that a universal of set of parameters, which is independent of vegetation cover type and characteristics can be adopted in prevalent LUE models. Availability of this well tested and universal set of parameters would help to improve the accuracy and applicability of LUE models in various biomes and geographic regions. [ABSTRACT FROM AUTHOR]

Numerous normalized difference spectral indices (NDSIs) derived from leaf measurements and CHRIS/PROBA hyperspectral and multi-angular satellite images were examined for their capacity to track seasonal variations of leaf ( ε leaf ) and canopy ( ε can ) light use efficiency of a Mediterranean phryganic ecosystem. A series of seasonal field ecophysiological measurements, i.e. leaf area index (LAI), leaf photosynthesis and leaf reflectance, were conducted on the Phlomis fruticosa shrubs at the days of CHRIS acquisitions over the study site. Leaf scale analysis confirmed background theory on the relationship of the photochemical reflectance index (PRI) with ε leaf and provided a detailed view of the wavelengths that can be used in PRI formulation for the specific species. In canopy scale analysis, PRI and some alternative formulations of this index based on CHRIS bands, presented the most significant relationships with ε can , indicating that this index preserves its efficiency in satellite observations for the specific ecosystem. Additionally, spectral indices related to chlorophyll and water content were found to present good relationships with ε can . Taking into account the functional relationship between ε can and chlorophyll content, a combination of the xanthophyll de-epoxidation band (531 nm) with 701 nm CHRIS band in a NDSI is suggested as an alternative to the original PRI formulation that could improve seasonal ε can estimations. The satellite observation geometry effects on the determination of ε can were not very intense for the studied ecosystem. However, the most effective viewing direction was proved to be the backward scattering, while zenith observations were the least efficient for the specific ecosystem, most probably due to increased background effects. Even though the sensitivity of the original PRI formulation to ε can was reduced in forward scattering viewing directions, when 531 nm xanthophyll de-epoxidation band was replaced with higher wavelength bands (540–550 nm), a strong PRI– ε can relationship reappeared. These findings indicate possible shift of xanthophyll de-epoxidation signal according to viewing direction. [ABSTRACT FROM AUTHOR]