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Kura clover (Trifolium ambiguum Bieb.) perennial living mulch is a farming practice designed to pair water quality services and row crop production. In this system, rows are mechanically or chemically killed within a perennial kura clover stand to create a suitable environment for planting a crop. However, yields are often reduced relative to conventional, fully-tilled systems. This hypothesis was tested by using a novel rotary zone tillage unit (RZT) that creates 45 cm wide tilled strips with a depth of 8 cm for each row, with rows spaced 76 cm apart. We compared RZT to three row preparation strategies that have been commonly used in living mulch systems: herbicide band kill (BK), and shank tillage (ST), also known as strip tillage. Shank tillage produced bands that were 40 cm wide and 5 cm deep. We monitored kura clover health, soil moisture & temperature, corn (Zea mays L.) emergence, corn development, and corn yield in all three row preparation strategies over two growing seasons. In 2015, corn grown in RZT plots emerged and developed faster than corn grown in ST plots, but this did not lead to a difference in grain or stover yield. However, in 2016, corn grown in RZT plots not only emerged and developed faster, but also produced 4.0 Mg ha−1 more grain and 3.5 Mg ha−1 more stover biomass than corn grown in ST and BK plots. Kura clover biomass was not affected by treatment in either year. We conclude that rotary zone tillage is a promising row preparation strategy in kura clover living mulch for corn production with minimal herbicide use.

Zone tillage is a reduced-tillage approach that attempts to capture both the environmental advantages of year-round ground cover and the agronomic benefits of in-row tillage. This study was conducted to determine the effect of differing levels of zone tillage intensity on soil carbon and nitrogen cycling in a corn-kura clover cropping system (Zea maize-Trifolium ambiguum). Research took place in Rosemount, MN in 2015 and 2016 in an established kura clover stand. Soils and kura clover biomass were each sampled three times in crop rows per year in four treatments that varied by intensity: NT (spray-down no-till), ST (shank-till, traditional strip till unit), RZT (zone-till, PTO-driven rotary zone tiller), and DT (double-till, ST + RZT). Samples were analyzed for microbial biomass (MB), soil inorganic nitrogen, and permanganate oxidizable carbon (POXC). Additionally, potentially mineralizable nitrogen (PMN) was measured for 2016 post-spring tillage soils. Greater spring kura clover biomass in 2016 (2449 kg ha−1) relative to 2015 (187 kg ha−1) influenced overall differences in soil quality between years. The double-till (DT) treatment had greater post-till soil inorganic N than the no-till (NT) treatment in 2016, and by corn harvest, both zone-till (RZT) and double-till (DT) had higher soil inorganic N than NT, indicating that the addition of kura clover biomass contributed to in-row, plant-available nitrogen. Double-till was also more effective in reducing kura clover encroachment into crop rows than NT. No effect of tillage intensity on PMN, MB, or POXC was observed at any sampling time, although trends of decreasing POXC paired with increasing MB over the 2016 growing season suggest that the quantity of incorporated kura clover biomass may have governed belowground nutrient cycling and soil fertility.

In-field estimation of alfalfa (Medicago sativa L.) yield and nutritive value can inform management decisions to optimize forage quality and production. However, acquisition of timely information at the field scale is limited using traditional measurements such as destructive sampling and assessment of plant maturity. Remote sensing technologies (e.g., measurement of canopy reflectance) have the potential to enable rapid measurements at the field scale. Canopy reflectance (350–2500 nm) and Light Detection and Ranging (LiDAR)-estimated canopy height were measured in conjunction with destructive sampling of alfalfa across a range of maturities at Rosemount, MN in 2014 and 2015. Sets of specific spectral wavebands were determined via stepwise regression to predict alfalfa yield and nutritive value and models were reduced by spectral range to improve utility. Cumulative growing degree units (GDUs) and canopy height were tested as model covariates. An alternative GDU calculation (GDUALT) using a temporally graduating base temperature was also tested against the traditional static base temperature. The inclusion of GDUALT increased prediction accuracy for all response variables by 9–17%. Models using a common set of seven wavebands, combined with GDUALT, explained 81–90% of the variability in yield, crude protein (CP), neutral detergent fiber (NDF), and NDF digestibility (NDFd; 48-h in-vitro), respectively. This research establishes potential for remote sensing measurements to be integrated with air temperature information to achieve rapid and accurate predictions of alfalfa yield and nutritive value at the field scale for optimized harvest management.

Improvement of process-based crop models is needed to achieve high fidelity forecasts of regional energy, water, and carbon exchanges. However, most state-of-the-art Land Surface Models (LSMs) assessed in the fifth phase of the Coupled Model Inter-comparison project (CMIP5) simulated crops as unmanaged C3 or C4 grasses. This study evaluated the crop-enabled version of one of the most widely used LSMs, the Community Land Model (CLM4-Crop), for simulating corn and soybean agro-ecosystems at relatively long-time scales (up to 11 years) using 54 site-years of data. We found that CLM4-Crop had a biased phenology during the early growing season and that carbon emissions from corn and soybean were underestimated. The model adopts universal physiological parameters for all crop types neglecting the fact that different crops have different specific leaf area, leaf nitrogen content and vcmax25, etc. As a result, model performance varied considerably according to crop type. Overall, the energy and carbon exchange of corn systems were better simulated than soybean systems. Long-term simulations at multiple sites showed that gross primary production (GPP) was consistently over-estimated at soybean sites leading to very large short and long-term biases. A modified model, CLM4-CropM’, with optimized phenology and calibrated crop physiological parameters yielded significantly better simulations of gross primary production (GPP), ecosystem respiration (ER) and leaf area index (LAI) at both short (hourly) and long-term (annual to decadal) timescales for both soybean and corn.

Dairy farms in the United States are diverse and although regional dairy production and farm strategy has been evaluated for environmental impact, a comprehensive national assessment is important to define national priorities for sustainable intensification. We estimated important environmental footprints of dairy production using process-level simulation and cradle-to-farm gate life cycle assessment. Dairy farms representing the sizes and management practices found in six regions were simulated with the Integrated Farm System Model (IFSM). Regional and national environmental footprints were determined as an average of all simulated farms weighted by the portion of milk contributed by each to the total. Nationally, dairy farms were found to emit 99,000 ± 8480 Gg CO2e of greenhouse gas (GHG), equivalent to 1.5% of the estimated U.S. total GHG emission, with a commodity-based intensity of 1.01 ± 0.09 kg CO2e/kg of fat and protein corrected milk (FPCM) produced. Fossil energy use was 242,700 ± 38,400 TJ, 0.3% of the U.S. total, or 2.48 ± 0.39 MJ/kg FPCM. Blue (non-precipitation) water use was 11,600 ± 2480 Tg, roughly 3.0% of the estimated U.S. total freshwater withdraw, with an intensity of 119 ± 25 kg/kg FPCM. While these environmental footprints represent a small portion of the respective national inventories, the dairy industry's contribution to reactive nitrogen (N) losses appears to be considerably greater. Losses of reactive N were estimated at 970 ± 133 Gg, with an intensity of 9.92 ± 1.36 g/kg FPCM where 66% of the total was in the form of ammonia (NH3). Although there are no national estimates of total reactive N loss, dairy farms were found to contribute 19–24% of national inventories of NH3 emissions. While strategies are available to reduce NH3 emissions, finding economical and sustainable solutions that do not result in pollution swapping remains a challenge for the dairy industry.

Intensification of the US dairy industry has driven increased reliance on maize (Zea mays L.) silage as a primary forage source in place of perennial forages such as alfalfa (Medicago sativa L.). Using 29 site-years of eddy covariance, plant, and manure measurements, we calculated net ecosystem C balances (NECB) for two silage maize-based forage cropping systems and a soybean-maize grain rotation. We found that C losses were over threefold greater from continuous silage maize (-4.9 Mg C ha−1 yr−1) than from the predominant grain cropping system in the region, the soybean-maize rotation (-1.3 Mg C ha−1 yr−1). Including alfalfa in rotation reduced C losses by 23% relative to continuous silage maize, but net losses were still observed (-3.8 Mg C ha−1 yr−1). For every megagram of crop residue C left in-field, net C balances increased by +0.9 Mg C ha−1. A winter rye (Secale cereale L.) cover crop and applications of liquid dairy manure marginally improved C-balances but were insufficient to offset C losses in respiration and crop harvest. Increasing manure application rates could bring these systems to a net equilibrium C balance but would also result in soil N and P surpluses and unacceptable loss of nutrients to air and water. Since 1980, over 800,000 hectares of alfalfa have been lost across the Upper Midwest US, and C export in harvested maize grain and silage have increased dramatically. This shift implies a substantial reduction in SOC on forage cropped soils in the region.

Anthropogenic emissions of nitrous oxide (N 2 O), a trace gas with severe environmental costs, are greatest from agricultural soils amended with nitrogen (N) fertilizer. However, accurate N 2 O emission estimates at fine spatial scales are made difficult by their high variability, which represents a critical challenge for the management of N 2 O emissions. Here, static chamber measurements ( n = 60) and soil samples ( n = 129) were collected at approximately weekly intervals ( n = 6) for 42-d immediately following the application of N in a southern Minnesota cornfield (15.6-ha), typical of the systems prevalent throughout the U.S. Corn Belt. These data were integrated into a geostatistical model that resolved N 2 O emissions at a high spatial resolution (1-m). Field-scale N 2 O emissions exhibited a high degree of spatial variability, and were partitioned into three classes of emission strength: hotspots, intermediate, and coldspots. Rates of emission from hotspots were 2-fold greater than non-hotspot locations. Consequently, 36% of the field-scale emissions could be attributed to hotspots, despite representing only 21% of the total field area. Variations in elevation caused hotspots to develop in predictable locations, which were prone to nutrient and moisture accumulation caused by terrain focusing. Because these features are relatively static, our data and analyses indicate that targeted management of hotspots could efficiently reduce field-scale emissions by as much 17%, a significant benefit considering the deleterious effects of atmospheric N 2 O.

Tropical peatlands are a major, but understudied, biophysical feedback factor on the atmospheric greenhouse effect. The largest expanses of tropical peatlands are located in lowland areas of Southeast Asia and the Amazon basin. The Loreto Region of Amazonian Peru contains ~63,000 km2 of peatlands. However, little is known about the biogeochemistry of these peatlands, and in particular, the cycling of carbon dioxide (CO2) and methane (CH4), and their responses to hydrometeorological forcings. To address these knowledge gaps, we established an eddy covariance (EC) flux tower in a natural palm (Mauritia flexuosa L.f.) swamp peatland near Iquitos, Peru. Here, we report ecosystem-scale CO2 and CH4 flux observations for this Amazonian palm swamp peatland over a two-year period in relation to hydrometeorological forcings. Seasonal and short-term variations in hydrometeorological forcing had a strong effect on CO2 and CH4 fluxes. High air temperature and vapor pressure deficit (VPD) exerted an important limitation on photosynthesis during the dry season, while latent heat flux appeared to be insensitive to these climate drivers. Evidence from light-response analyses and flux partitioning support that photosynthetic activity was downregulated during dry conditions, while ecosystem respiration (RE) was either inhibited or enhanced depending on water table position. The cumulative net ecosystem CO2 exchange indicated that the peatland was a significant CO2 sink ranging from −465 (−279 to −651) g C m−2 y−1 in 2018 to −462 (−277 to −647) g C m−2 y−1 in 2019. The forest was a CH4 source of 22 (20 to 24) g C m−2 y−1, similar in magnitude to other tropical peatlands and larger than boreal and arctic peatlands. Thus, the annual carbon budget of this Amazonian palm swamp peatland appears to be a major carbon sink under current hydrometeorological conditions.

Nutrient recycling is fundamental to sustainable agricultural systems, but few mechanisms exist to ensure that surplus manure nutrients from animal feeding operations are transported for use on nutrient-deficient croplands. As a result, manure nutrients concentrate in locations where they can threaten environmental health and devalue manure as a fertilizer resource. This study advances the concept of the “manureshed” – the lands surrounding animal feeding operations onto which manure nutrients can be redistributed to meet environmental, production, and economic goals. Manuresheds can be managed at multiple scales, for example, on farms with both animals and crops, among animal farms and crop farms within a county, or even among animal farms and crop farms in distant counties. With a focus on redistribution among counties, we classified the 3109 counties of the contiguous United States by their capacity to either supply manure phosphorus (P) and nitrogen (N) from confined livestock production (“sources”) or to assimilate and remove excess P and N via crops (“sinks”). Manure nutrient source counties were identified in 40 of the 48 states, with a substantial concentration in the southern US. Source counties for manure P greatly outnumbered source counties for manure N (390 vs. 100), and 99 of the 100 manure N source counties were also source counties for manure P. Conversely, sink counties for manure N outnumbered sink counties for manure P (2766 vs. 2317). We used the P balances of the source and sink counties to delineate four manuresheds dominated by various combinations of confined hog, poultry, dairy, and beef industries. The four manuresheds differed in the transport distances needed to assimilate excess manure P from their respective source areas (from 147 ± 51 km for a beef dominated manureshed to 368 ± 140 km for a poultry dominated manureshed), highlighting the need for systems-level strategies to promote manure nutrient recycling that operate across local, county, regional, and national scales.

All-black thermopile pyranometers are commonly used to measure solar radiation. Ensuring that the sensors are stable and free of drift is critical to accurately measure small variations in global solar irradiance at the Earth’s surface (K↓), which is a potential driver of changes in surface temperature. We demonstrate that the decreased responsivities of Eppley PSP pyranometers of −1.5% y−1, or −0.38% (GJ m−2)−1, were accompanied by a change in its spectral response owing to a discoloration of the sensing element. These observations motivated further work to develop routines to detect probable pyranometer drift in historical time-series. The temporal trends in the following ratios were used to detect pyranometer sensor drift: photosynthetically active radiation (PAR) to K↓, K↓ to KEX (extraterrestrial radiation at the top of the atmosphere) and PAR to KEX. Data from 8 AmeriFlux sites spanning latitudes from ∼32 to 54°N were examined in this analysis. Probable drift in either a pyranometer or PAR sensor was identified at 5 of the 8 sites. The magnitude of the drift represented changes of 0.15–0.85% y−1, which is sufficient to obscure actual trends in K↓, although these should be considered conservative low end drift estimates, given that we were not making comparisons to co-located higher grade instruments. Deployment exposure errors caused by sensor shading were also discovered by comparing the daily correlations between (i) K↓ and KEX and (ii) PAR and KEX. Sensors drifting at rates similar to our defective PSP over a 5 year period would contribute to an underestimation of available energy of ∼70 W m−2, which is non-trivial in the context of assessing eddy covariance energy balance closure, employing Penman-Monteith or Bowen ratio methods or calculating albedo radiative forcings. Given that probable drift was identified at multiple AmeriFlux sites, we recommend enhancing network access to calibration services that are traceable to a high quality gold standard.

Wetlands represent the dominant natural source of methane (CH(4)) to the atmosphere. Thus, substantial effort has been spent examining the CH(4) budgets of global wetlands via continuous ecosystem-scale measurements using the eddy covariance (EC) technique. Robust error characterization for such measurements, however, remains a major challenge. Here, we quantify systematic, random and gap-filling errors and the resulting uncertainty in CH(4) fluxes using a 3.5 year time series of simultaneous open- and closed path CH(4) flux measurements over a sub-boreal wetland. After correcting for high- and low frequency flux attenuation, the magnitude of systematic frequency response errors were negligible relative to other uncertainties. Based on three different random flux error estimations, we found that errors of the CH(4) flux measurement systems were smaller in magnitude than errors associated with the turbulent transport and flux footprint heterogeneity. Errors on individual half-hourly CH(4) fluxes were typically 6%–41%, but not normally distributed (leptokurtic), and thus need to be appropriately characterized when fluxes are compared to chamber-derived or modeled CH(4) fluxes. Integrated annual fluxes were only moderately sensitive to gap-filling, based on an evaluation of 4 different methods. Calculated budgets agreed on average to within 7% (≤ 1.5 g − CH(4) m(−2) yr(−1)). Marginal distribution sampling using open source code was among the best-performing of all the evaluated gap-filling approaches and it is therefore recommended given its transparency and reproducibility. Overall, estimates of annual CH(4) emissions for both EC systems were in excellent agreement (within 0.6 g − CH(4) m(−2) yr(−1)) and averaged 18 g − CH(4) m(−2) yr(−1). Total uncertainties on the annual fluxes were larger than the uncertainty of the flux measurement systems and estimated between 7–17%. Identifying trends and differences among sites or site years requires that the observed variability exceeds these uncertainties.

Real-world applications will inevitably entail divergence between samples on which chemometric classifiers are trained and the unknowns requiring classification. This has long been recognized, but there is a shortage of empirical studies on which classifiers perform best in 'external validation' (EV), where the unknown samples are subject to sources of variation relative to the population used to train the classifier. Survey of 286 classification studies in analytical chemistry found only 6.6% that stated elements of variance between training and test samples. Instead, most tested classifiers using hold-outs or resampling (usually cross-validation) from the same population used in training. The present study evaluated a wide range of classifiers on NMR and mass spectra of plant and food materials, from four projects with different data properties (e.g., different numbers and prevalence of classes) and classification objectives. Use of cross-validation was found to be optimistic relative to EV on samples of different provenance to the training set (e.g., different genotypes, different growth conditions, different seasons of crop harvest). For classifier evaluations across the diverse tasks, we used ranks-based non-parametric comparisons, and permutation-based significance tests. Although latent variable methods (e.g., PLSDA) were used in 64% of the surveyed papers, they were among the less successful classifiers in EV, and orthogonal signal correction was counterproductive. Instead, the best EV performances were obtained with machine learning schemes that coped with the high dimensionality (914-1898 features). Random forests confirmed their resilience to high dimensionality, as best overall performers on the full data, despite being used in only 4.5% of the surveyed papers. Most other machine learning classifiers were improved by a feature selection filter (ReliefF), but still did not out-perform random forests.

Meeting the competing demands for freshwater of the urban, industrial, and agricultural communities is increasingly challenging as the global population continues to grow and the need for potable water, food, fiber, and fuel grows with it. One of the keys to meeting these demands is maximizing the efficiency of water use in agricultural applications. Toward this end, a thorough understanding of the factors driving evapotranspiration and their response to spatiotemporal variations in local environmental conditions is needed for the development and validation of numerical and remote sensing-based models. Moreover, because these exchange processes are strongly nonlinear, scaling measurements collected at one scale to another remains a nontrivial task. In an effort to identify the key environmental drivers controlling the latent heat flux (λE) from agro-ecosystems and their potential impacts on upscaling in-situ flux measurements, eddy covariance and micrometeorological data collected over maize and soy at three distinct sites located in Maryland, Iowa, and Minnesota, respectively were evaluated for the years between 2007 and 2011. The magnitudes of the evaporative fluxes were comparable for measurements collected during clear-sky days with similar environmental conditions; on average, the measurements of λE agreed to within 50 W m -2 , or approximately 10%. When considered in terms of evaporative fraction ( f e ), however, there were marked differences among the sites. For example, while the magnitude and diurnal pattern of f e for mature maize at the Minnesota site was nearly constant ( f e = 0.66) during the day, f e at both the Maryland and Iowa site increased steadily during the day from a minimum value near 0.68 at midmorning to peak value of 0.87 in the afternoon. These differences appear to be primarily linked to differences in soil moisture and vegetation density at the various sites. As such, this research underscores the impact of local environmental conditions in controlling land-atmosphere exchange processes. It also underscores the importance accurately describing local environmental conditions when modeling surface fluxes.

article i nfo An accurate and synopticquantification of gross primary production (GPP) incrops isessential for studies of car- bon budgets at regional and global scales. In this study, we tested a model, relating crop GPP to a product of total canopy chlorophyll (Chl) content and potential incident photosynthetically active radiation (PARpotential). The approach isbased on remotely sensed data;specifically, vegetation indices (VI) thatare proxiesfor total Chlcon- tent and PARpotential, which is incident PAR under a condition of minimal atmospheric aerosol loading. Using VI retrieved from surface reflectance Landsat data, we found that the model is capable of accurately estimating GPP in maize, with coefficient of variation (CV) below 23%, and in soybean with CV below 30%. The algorithms established and calibrated over three Mead, Nebraska AmeriFlux sites were able to estimate maize and soybean GPP at tower flux sites in Minnesota, Iowa and Illinois with acceptable accuracy.

The stable carbon isotope ratio, CO 13 2 / CO 12 2 , is a valuable tracer for studying the processes controlling the autotrophic (FRa) and heterotrophic (FRh) contributions to ecosystem respiration (FR) and the influence of photosynthesis on FR. There is increasing interest in quantifying the temporal variability of the carbon isotope composition of ecosystem respiration (δR) because it contains information about the sources contributing to respiration and is an important parameter used for partitioning net ecosystem CO2 exchange using stable isotope methods. In this study, eddy covariance, flux gradient, automated chambers, and stable carbon isotope techniques were used to quantify and improve our understanding of the temporal variability in FR and δR in a C3/C4 agricultural ecosystem. Six years (2004–2009) of isotope flux-gradient measurements indicated that δR had a very consistent annual pattern during both C3 (soybean) and C4 (corn) growing seasons due to significant contributions from FRa, which was strongly influenced by the isotope composition of the recent photosynthate. However, in the spring, δR exhibited a C3 signal regardless of the crop grown in the previous season. One hypothesis for this anomaly is that at these low soil temperatures microbial activity relied predominantly on C3 substrates. Automated chamber measurements of soil respiration ( F R s ) and its isotope composition ( δ R s ) were initiated in the early corn growing season of 2009 to help interpret the variability in δR. These measurements showed good agreement with EC measurements of FR (within 0.5 μmol m2 s−1) and isotope flux gradient measurements of δR (within 2‰) at nighttime for near-bare soil conditions (LAI δ R s . The relatively enriched signal above the canopy indicates that δR was strongly influenced by aboveground plant respiration (FR,ag), which accounted for about 40% of FR. The automated chamber data and analyses also revealed a strong diel pattern in δ R s . In the early growth period, δ R s showed a sharp morning enrichment of up to 4‰ followed by a gradual depletion throughout the afternoon and evening. Daytime enrichment in δ R s was most pronounced during dry conditions and was not observed when the upper soil was near saturation. We provide anecdotal evidence that the diel variability during early growth may have been influenced by turbulence (advection/non-diffusive transport), which reduced the kinetic fractionation effect. At peak growth, there is evidence that the sheltering effect of the corn plants diminished the influence of turbulence on the chamber measurement of δ R s . Further research is needed to evaluate and separate the contributions of biotic and abiotic (advection and non-steady state effects) influences on chamber δ R s observations.

Separation of the photosynthetic ( F P ) and respiratory ( F R ) fluxes of net CO 2 exchange ( F N ) remains a necessary step toward understanding the biological and physical controls on carbon cycling between the soil, biomass, and atmosphere. Despite recent advancements in stable carbon isotope partitioning methodology, several potential limitations can cause uncertainty in the partitioned results. Here, we combined an automated chamber system with a tunable diode laser (TDL) to evaluate carbon isotope partitioning under controlled environmental conditions. Experiments were conducted in a climate controlled greenhouse utilizing both soybean (C 3 pathway) and corn (C 4 pathway) treatments. Under these conditions, net exchange of CO 13 2 and CO 12 2 was obtained with an improved signal to noise ratio. Further, the chamber system was used to estimate soil evaporation ( E ) and plant transpiration ( T ), allowing for an improved estimate of the total conductance to CO 2 ( g c ). This study found that the incorporation of short-term and diel variability in the isotope composition of respiration ( δ R ) caused F P to nearly double in the corn system while only slightly increasing in the soybean system. Variability in both g c and the CO 2 bundle sheath leakage factor for C 4 plants ( ϕ ) also had a significant influence on F P . In addition, chamber measurements of F N and its isotope composition ( δ N ) indicated that post-illumination processes caused a decrease in plant respiration for up to 3 h following light termination. Finally, this study found systematic differences between the isotope and temperature-regression partitioning methods on the diel time scale.

Routine field calibration of water vapor analyzers has always been a challenging problem for those making long-term flux measurements at remote sites. Automated sampling of standard gases from compressed tanks, the method of choice for CO 2 calibration, cannot be used for H 2 O. Calibrations are typically done manually with a dew point generator, and as a consequence are done less frequently, degrading the accuracy and utility of H 2 O flux data. To remedy this, we have designed, built and tested a field-portable mixing ratio generator, also including features that facilitate its use in water vapor isotope research. It consists of an aluminum cell, with a usable capacity of 80 cm 3 , that is coupled to a Peltier heater/cooler and encased in insulation. The temperature of water in the cell is monitored with a thermocouple and a pressure transducer is used to measure the cell pressure. A data logger uses this information to compute the mixing ratio in the cell and control the polarity and duty cycle of the power input to the Peltier block in order to drive the system toward the desired mixing ratio and to maintain it. Incoming air enters through a bubbler and the exiting air is directed to the gas analyzer. A separate fitting mates to a syringe, allowing easy filling and draining of the cell. The cost of the components, exclusive of the data logger, is approximately 1100 USD. Power consumption is low, on the order of 10 W, facilitating use at remote sites. As a consequence, the response time of the instrument is relatively slow, but could be substantially improved for situations where dynamic response is more important than power consumption. Testing has shown that the unit is accurate over a broad range of mixing ratios, able to compensate for changes in ambient pressure, and stable for long periods of time. In addition, it has been used to generate Rayleigh distillation plots, demonstrating its utility for routinely checking the performance of water vapor isotope lasers.

Climate change and economic concerns have motivated intense interest in the development of renewable energy sources, including fuels derived from plant biomass. However, the specter of massive biofuel production has raised other worries, specifically that by displacing food production it will lead to higher food prices, increased incidence of famine, and acceleration of undesirable land use change. One proposed solution is to increase the annual net primary productivity of the existing agricultural land base, so that it can sustainably produce both food and biofuel feedstocks. This might be possible in corn and soybean production regions through the use of winter cover crops, but the biophysical feasibility of this has not been systematically explored. We developed a model for this purpose that simulates the potential biomass production and water use of winter rye in continuous corn and corn–soybean rotations. The input data requirements represent an attempt to balance the demands of a physically and physiologically defensible simulation with the need for broad applicability in space and time. The necessary meteorological data are obtainable from standard agricultural weather stations, and the required management data are simply planting dates and harvest dates for corn and soybeans. Physiological parameters for rye were taken from the literature, supplemented by experimental data specifically collected for this project. The model was run for a number of growing seasons for 8 locations across the Midwestern USA. Results indicate potential rye biomass production of 1–8 Mg ha−1, with the lowest yields at the more northern sites, where both PAR and degree-days are limited in the interval between fall corn harvest and spring corn or soybean planting. At all sites rye yields are substantially greater when the following crop is soybean rather than corn, since soybean is planted later. Not surprisingly, soil moisture depletion is most likely in years and sites where rye biomass production is greatest. Consistent production of both food and biomass from corn/winter rye/soybean systems will probably require irrigation in many areas and additional N fertilizer, creating possible environmental concerns. Rye growth limitations in the northern portion of the corn belt may be partially mitigated with aerial seeding of rye into standing corn.

Agricultural ecosystems have been viewed with the potential to sequester atmospheric carbon dioxide (CO 2 ) by increasing soil organic carbon (SOC) through reduced tillage and cover cropping practices. There remains considerable uncertainty, however, regarding the carbon (C) sink/source potential of these systems and few studies have examined C dynamics in conjunction with other important greenhouse gases. The objective of this study was to evaluate the impact of an alternative management scenario (reduced tillage and cover cropping) on ecosystem respiration ( R E ) and nitrous oxide (N 2 O) and methane (CH 4 ) fluxes in a maize ( Zea mays L.)/soybean ( Glycine max L.) rotation ecosystem in east-central Minnesota, United States. The control treatment was managed using fall tillage with a chisel plow in combination with a tandem disk, and the experimental treatment was managed using strip tillage and a winter rye ( Secal cereale ) cover crop. Over the two-year study period (2004–2005), cumulative R E was 222.7 g C m −2 higher in the alternatively managed treatment as a result of increased decomposition of the cover crop residue. N 2 O fluxes were similar in both treatments during the 2004 growing season and were 100.1 mg N m −2 higher in the conventional treatment during the 2005 growing season after nitrogen (N) fertilization. N fertilization and fertilizer type were the dominant factors controlling N 2 O fluxes in both treatments. CH 4 fluxes were negligible in both treatments and often below the detection limit. Cumulative growing season N 2 O losses in the control and experimental treatments, which totalled 38.9 ± 3.1 and 26.1 ± 1.7 g C m −2 when converted to CO 2 equivalents, were comparable to the annual estimates of net ecosystem CO 2 exchange in both treatments. This study further supports that N 2 O losses are an important component of the total greenhouse gas budget of agroecosystems. It also suggests that spring cover cropping, without residue removal, has limited C sequestration potential. The results from this study, however, may not necessarily represent equilibrium conditions in the experimental treatment. Rather, they are a measure of the transient response of the system after tillage conversion and cover crop addition. It is expected that the soil microbes will continue to adjust to the reduction in tillage and increased C inputs. Therefore, continued, long-term monitoring is needed to confirm whether the results are representative of equilibrium conditions.

A potential abatement to increasing levels of carbon dioxide (CO(2)) in the atmosphere is the use of pyrolysis to convert vegetative biomass into a more stable form of carbon (biochar) that could then be applied to the soil. However, the impacts of pyrolysis biochar on the soil system need to be assessed before initiating large scale biochar applications to agricultural fields. We compared CO(2) respiration, nitrous oxide (N(2)O) production, methane (CH(4)) oxidation and herbicide retention and transformation through laboratory incubations at field capacity in a Minnesota soil (Waukegan silt loam) with and without added biochar. CO(2) originating from the biochar needs to be subtracted from the soil-biochar combination in order to elucidate the impact of biochar on soil respiration. After this correction, biochar amendments reduced CO(2) production for all amendment levels tested (2, 5, 10, 20, 40 and 60% w/w; corresponding to 24-720 tha(-1) field application rates). In addition, biochar additions suppressed N(2)O production at all levels. However, these reductions were only significant at biochar amendment levels20% w/w. Biochar additions also significantly suppressed ambient CH(4) oxidation at all levels compared to unamended soil. The addition of biochar (5% w/w) to soil increased the sorption of atrazine and acetochlor compared to non-amended soils, resulting in decreased dissipation rates of these herbicides. The recalcitrance of the biochar suggests that it could be a viable carbon sequestration strategy, and might provide substantial net greenhouse gas benefits if the reductions in N(2)O production are lasting.

Concerns over the safety of GM crops have led to detailed comparisons of their composition and performance with those of conventionally bred crops, under both field and glasshouse conditions. Such studies of wheat have shown that it is possible to develop GM lines which are substantially equivalent to conventional varieties. This information is important to inform the debate on the future development of GM food crops.

It is widely believed that soil disturbance by tillage was a primary cause of the historical loss of soil organic carbon (SOC) in North America, and that substantial SOC sequestration can be accomplished by changing from conventional plowing to less intensive methods known as conservation tillage. This is based on experiments where changes in carbon storage have been estimated through soil sampling of tillage trials. However, sampling protocol may have biased the results. In essentially all cases where conservation tillage was found to sequester C, soils were only sampled to a depth of 30 cm or less, even though crop roots often extend much deeper. In the few studies where sampling extended deeper than 30 cm, conservation tillage has shown no consistent accrual of SOC, instead showing a difference in the distribution of SOC, with higher concentrations near the surface in conservation tillage and higher concentrations in deeper layers under conventional tillage. These contrasting results may be due to tillage-induced differences in thermal and physical conditions that affect root growth and distribution. Long-term, continuous gas exchange measurements have also been unable to detect C gain due to reduced tillage. Though there are other good reasons to use conservation tillage, evidence that it promotes C sequestration is not compelling.

Soil organic carbon (SOC) and nitrogen (N) are directly influenced by tillage, residue return and N fertilization management practices. Soil samples for SOC and N analyses, obtained from a 23-year field experiment, provided an assessment of nearequilibrium SOC and N conditions. Crops included corn (Zea mays L.) and soybean [Glycine max L. (Merrill)]. Treatments of conventional and conservation tillage, residue stover (returned or harvested) and two N fertilization rates were imposed on a Waukegan silt loam (fine-silty over skeletal, mixed, superactive, mesic Typic Hapludoll) at Rosemount, MN. The surface (0‐ 20 cm) soils with no-tillage (NT) had greater than 30% more SOC and N than moldboard plow (MB) and chisel plow (CH) tillage treatments. The trend was reversed at 20‐25 cm soil depths, where significantly more SOC and N were found in MB treatments (26 and 1.5 Mg SOC and N ha � 1 , respectively) than with NT (13 and 1.2 Mg SOC and N ha � 1 , respectively), possibly due to residues buried by inversion. The summation of soil SOC over depth to 50 cm did not vary among tillage treatments; N by summation was higher in NT than MB treatments. Returned residue plots generally stored more SOC and N than in plots where residue was harvested. Nitrogen fertilization generally did not influence SOC or N at most soil depths. These results have significant implications on how specific management practices maximize SOC storage and minimize potential N losses. Our results further suggest different sampling protocols may lead to different and confusing conclusions regarding the impact of tillage systems on C sequestration. Published by Elsevier B.V.

Stable isotopes of carbon dioxide (CO2) can be used as natural tracers to better understand carbon cycle processes and exchange pathways between the biosphere and atmosphere. In this study, we used a tunable diode laser (TDL) technique for continuous fast measurement of the stable isotopomers C 18 O 16 O and C 16 O2 and their ratio, da. The TDL system was configured to measure mixing ratios of [C 16 O2] and [C 18 O 16 O] at wavenumber frequencies of 2308.225 and 2308.416 cm � 1 , respectively. Two-minute precision (1 standard deviation) was 0.0004, 0.09 mmol mol � 1 , and 0.26% for [C 18 O 16 O], [C 16 O2] and da, respectively. Comparison of TDL and mass spectrometry flask measurements showed relatively good agreement (r 2 = 0.94) with a standard deviation of 0.55% for the residual values. Estimates of the isotope signature of ecosystem flux components over a soybean (Glycine max) field were examined. These data represent one of the first continuous flux measurements of C 18 O 16 O. The isotope signature of net ecosystem CO2 exchange at night ranged from � 15 to � 7% and during the daytime from � 40 to � 20%. A simple estimate of canopy-scale photosynthetic discrimination showed significant diurnal variation and averaged 10.5% (� 8.8%). The large difference between the isotope signature of respiration and midday canopy photosynthesis represented significant isotopic disequilibrium. Coupled with recent advances in measuring water vapor isotopomers with the TDL technique, a new opportunity is emerging to better understand the dynamics, complex interactions, and discrimination mechanisms controlling land-atmosphere C 18 O 16 O exchange.

Ecosystem-atmosphere fluxes of 12 CO2 and 13 CO2 are needed to better understand the impacts of climate and land use change on ecosystem respiration (F R), net ecosystem CO2 exchange (F N), and canopy-scale photosynthetic discrimination (D). We combined micrometeorological and stable isotope techniques to quantify isotopic fluxes of 12 CO2 and 13 CO2 over a corn‐ soybean rotation ecosystem in the Upper Midwest United States. Results are reported for a 192-day period during the corn (C4) phase of the 2003 growing season. The isotopomer flux ratio, d 13 CO2/d 12 CO2, was measured continuously using a tunable diode laser (TDL) and gradient technique to quantify the isotope ratios of F R (dR) and F N (dN). Prior to leaf emergence dR was approximately � 26%. It increased rapidly following leaf emergence and reached an average value of � 12.5% at full canopy. dR decreased to pre-emergence values following senescence. dN also showed strong seasonal variation and during the main growing period averaged � 11.6%. dR and dN values were used in a modified flux partitioning approach to estimate canopy-scale D and the isotope ratio of photosynthetically assimilated CO2 (dP) independent of calculating canopy conductance or assuming leafscale discrimination factors. The results showed substantial day-to-day variation in D with an average value of 4.0%. This fluxbased estimate of D was approximately 6% lower than the Keeling mixing model estimate and in better agreement with leaflevel observations. These data were used to help constrain and partition F R into its autotrophic (F Ra) and heterotrophic (F Rh) components based on the numerical optimization of a mass balance model. On average F Ra accounted for 44% of growing season F R and reached a maximum of 59% during peak growth. The isotope ratio of F Rh (dRh), was � 26% prior to leaf emergence, and became increasingly 13 C enriched as the canopy developed indicating that recent photosynthate became the dominant substrate for microbial activity. Sensitivity analyses substantiated that F Rh had a major influence on the seasonal pattern of dR, dN and the isotopic disequilibrium of the ecosystem. These data and parameter estimates are critical for validating and constraining the parameterization of land surface schemes and inverse models that aim to estimate regional carbon sinks and

The combination of micrometeorological and stable isotope techniques offers a relatively new approach for elucidating ecosystem-scale processes. Here we combined a micrometeorological gradient technique with tunable diode laser absorption spectroscopy (TDLAS) using the Trace Gas Analyzer (TGA100, Campbell Scientific, Inc., Utah, USA) to measure field-scale isotopic CO2 mixing ratios and fluxes of 12 CO 2 and 13 CO 2 . The experiment was conducted in a recently harvested soybean (Glycine max) field that had been in corn (Zea mays) production the previous 4 years. Measurements were made over a period of 26 days from October 25 to November 19, 2002. Weather conditions were unusually cold and dry during the experiment. Isotopic gradients were small and averaged −0.153 and −0.0018 μmol mol−1 m−1 for 12 CO 2 and 13 CO 2 , respectively for u ∗ >0.1 m s−1. The average 12 CO 2 and 13 CO 2 flux for the period was 1.0 and 0.012 μmol m−2 s−1, respectively. The isotope ratio of respired carbon ( δ 13 C R ) obtained from the linear intercept of a Keeling plot was −27.93‰ (±0.32‰) for the experimental period. The Keeling plot technique was compared to a new flux ratio methodology that estimates δ 13 C R from the slope of a linear plot of 13 CO 2 versus 12 CO 2 flux. This method eliminates a number of potential limitations associated with the Keeling plot and provides a δ 13 C R value that can be directly related to the flux footprint. In this initial comparison, our analysis showed that the flux ratio method produced a similar δ 13 C R value (−28.67‰), but with greater uncertainty (±2.1‰). Better results are expected during growing season conditions when fluxes are substantially larger and the signal to noise ratio is improved. The isotope ratio of respired carbon was consistent with C3 agricultural systems indicating that soybean decomposition was the dominant substrate for respiration. The observed increase in ecosystem respiration (RE) and decrease in δ 13 C R following tillage indicated that the incorporation of fresh soybean residue provided the major source for decomposition and further illustrates that the combination of micrometeorological and stable isotope techniques can be used to better interpret changes in carbon cycle processes. Long-term and continuous measurements of isotopic CO2 exchange using tunable diode laser absorption spectroscopy and micrometeorological techniques offers a new opportunity to study carbon cycle processes at the field-scale.

The rate constants for acetolysis of (2,2-difluorocyclopropyl)methyl tosylate, and (2,2-difluoro-3-methylcyclopropyl)methyl tosylate at 92 °C and of 1-(2,2-difluorocyclopropyl)ethyl tosylate at 42 °C are reported and the reactivities and regiochemistries of ring opening of these systems are discussed and compared with expectations based on computational results. The results are discussed in terms of the use of (2,2-difluorocyclopropyl)methyl systems as mechanistic probes.

Surface–atmosphere mercury fluxes are difficult to measure accurately. Current techniques include dynamic flux chambers and micrometeorological gradient and aerodynamic approaches, all of which have a number of intrinsic problems associated with them. We have adapted conditional sampling (relaxed eddy accumulation), a micrometeorological technique commonly used to measure other trace gas fluxes, to measure surface–air mercury fluxes. Our initial flux measurement campaign over an agricultural soil consisted of two 1-week measurement periods, and was longer in duration than previously reported mercury flux measurement periods. Fluxes during both measurement periods ranged between 190.5 (evolution) and –91.7 ng m −2 h −1 (deposition) with an average evolution of 9.67 ng m −2 h −1 . The data showed significant diurnal trends, weakly correlated with shallow soil temperatures and solar radiation. This initial trial run indicates that conditional sampling has much promise for the accurate quantification of both short and long-term mercury fluxes.

Fluorine substituent effects on the structure of oxirane and on the kinetic behavior of oxiranylcarbinyl radicals, as determined by DFT calculations, have been found to be similar to those observed for the analogous fluorinated cyclopropylcarbinyl radical systems. A structural and energetic analysis showed that a stereoelectronic effect involving preferential interaction of the semi-occupied atomic orbital of the radical with the weaker ring bond is the major factor that contributes to the regiochemistry of the ring opening of fluorinated oxiranylcarbinyl radicals. With low and potentially zero activation barriers, 3,3-difluorooxiranylcarbinyl radical and cation undergo ring opening with CO bond cleavage and CC cleavage, respectively.

Surface temperature is a crucial variable linking surface–atmospheric energy exchange, but it is difficult to measure accurately. Remote measurement by infrared (IR) thermometry is often the only viable choice, but is plagued by problems that limit its absolute accuracy. Primary among these are calibration shifts and an inability to eliminate or properly account for the influence of detector temperature on the measurement. We have developed a new approach that avoids these and other difficulties by making the measurement differentially, essentially providing continuous calibration. The system uses a conventional infrared thermometer (IRT) coupled to a rotary actuator so that its field of view can be periodically switched from the target of interest to a blackbody cavity, whose temperature is controlled with a Peltier block/controller board assembly and measured with carefully calibrated thermocouples. The blackbody temperature is controlled so that the detector output is the same when viewing the blackbody as it is when viewing the target surface. When this condition is satisfied the blackbody temperature and the brightness temperature of the target surface are equal, i.e. the thermal radiation emanating from each is the same. A prototype instrument, using a conventional IRT as the detector, was built and tested in the laboratory by using it to measure the surface temperature of a mineral oil reservoir that was cycled over a range of temperatures and independently monitored with calibrated thermocouples. Over a 24°C temperature range, the mean absolute error of the instrument was 0.04°C, and a regression against thermocouple-measured oil temperature yielded a slope of 1.002, intercept of −0.015°C, and r2 of 0.99998, substantially better than the performance of a conventional IRT subjected to the same tests. A field instrument was also built, based on these principles but with smaller components for lower power consumption and lower cost. In an important departure, it uses two IR detectors and a modified switching/control algorithm that provides improved dynamic response while maintaining the accuracy of the prototype. We conclude that continuously-calibrated IR thermometry (CC-IRT) is a viable means for improving the accuracy of radiometric temperature measurement.

The phase coherence between repeat-pass SAR observations of a tropical forest region was investigated as a source of information for biophysical characteristics in regenerating and undisturbed areas of forest. SAR imagery was acquired and interferometric coherence maps were derived for a 1-day repeat pair at C-band, using ERS-1 and ERS-2, and for 44-day and 132-day repeats at L-band, using JERS. The backscattering coefficient and coherence characteristics were analyzed in conjunction with biomass density estimates determined from field measurements and land cover characteristics derived from an annual sequence of Landsat TM imagery. The results demonstrate that repeat-pass coherence adds significantly to the information available from SAR imagery of tropical forest.

There is a continued need for simple, robust, yet accurate methods for measuring the surface/atmosphere exchange of a wide variety of trace gases and particulates. Conditional sampling is a relatively new method that has received increasing attention in recent years because it is related to theoretically attractive eddy covariance, but does not require a rapid response sensor for the covariate. It does require rapid measurement of the vertical wind speed, w, and sorting of sampled air into two separate lines based on the direction of w. As originally proposed, the flux was then calculated as F=βΔCσw, where ΔC is the mean difference in concentration between the upward and downward moving eddies, σw the standard deviation of the vertical wind speed, and β an empirical coefficient. Subsequent exposition showed that β was derivable from the statistics of joint Gaussian distribution, although field experiments have consistently found values in the range of 0.56 to 0.58, somewhat lower than the theoretical expectation of ≈0.62–0.63. Here, we reexamine the method, and show that if the flux is instead expressed as F=b1σw2, where b1 is the regression-estimated slope of the concentration vs. wind speed relation, then it is exactly equivalent to eddy covariance. The aim of conditional sampling then becomes an estimation of b1 as ΔC/ΔW. We show that this quantity has a consistent positive bias when samples are sorted simply into positive and negative excursions from mean w. Inclusion of a sampling deadband, symmetric about the mean w, improves the accuracy of the slope estimate and decreases its variance as well. A potential problem with conditional sampling, regardless of which formulation may be used, is the effect of random measurement error (noise) in the wind speed measurement. We show that this introduces systematic errors into conditional sampling, while eddy covariance measurements are unaffected. Direct and indirect assessments indicate that these errors are too small to be significant for the sonic anemometer that we used, but it is probably wise for practitioners of the method to make certain that such is the case for the instruments used in their particular systems. We conclude that conditional sampling is a maturing method, with an increasing body of evidence indicating that the underlying relationships between scalar concentration and wind speed are sufficiently robust to support widespread use.

Frozen precipitation has important implications for water quality and soil biology. Nutrients in landspread animal manure are transported to surface waters by snowmelt, and winter survival of forages often depends on snow cover. Further development of mechanistic snow behavior models would be assisted by improved measurements of the disappearance of water from snowpacks. We developed a system to measure the total water content (snow-water equivalent, SWE) of a snow cover based on attenuation of γ-rays. A mixed Eu-152, 154 source (about 70 MBq) was pushed through raceways which were placed on the soil surface prior to snowfall. Attenuation of the emitted radiation by solid and liquid water in snow was measured with a Ge detector held above the snow and a multichannel analyzer. Use of four energy peaks and solution of the six resulting equations reduced dependence of the measurement on source-detector geometry. In laboratory tests, measurements of a fixed water depth (30 mm) were constant to ±1.5 mm following displacement of the detector by 50 mm laterally and 100 mm vertically, a much larger repositioning error than occurs in the field. Field tests showed that the system detected melting conditions with greater sensitivity than was attained with collecting of snow cores. Errors in estimated SWE due to repositioning of the detector were about ±3 mm. Estimated energy balance terms were in reasonable agreement with observed melting during a field experiment. The new device will allow non-destructive SWE measurements to assess the influences of a number of agricultural management practices on winter hydrology.

Increasing concerns about atmospheric transport of agricultural chemicals and the diffuse emission of greenhouse gases are motivation for development of accurate, straightforward methods for measurement of trace gas fluxes. A recently proposed hypothesis suggests that the flux of a substance is directly proportional to the product of the standard deviation of the vertical wind velocity (σw) and the difference between mean concentrations of that substance in upward- and downward-moving eddies (ΔC). Reported simulations indicate that the coefficient of proportionality (β) is unaffected by atmospheric stability and may be similar for heat, water vapor, and, presumably, other scalars. This research was conducted to evaluate the validity of this hypothesis and the utility of the method at the farm field scale. The system we developed samples air from the immediate vicinity of a one-dimensional sonic anemometer. There are two sample lines; one is open only when vertical velocity (w) is positive, and the other is open only when w is negative. These lines are routed to a differential gas analyzer, which simultaneously measures ΔC for both CO2 and H2O. A data-logger samples w at 10 Hz, controls the solenoid valves, and computes σw. It also samples a fine-wire thermocouple to allow computation of sensible heat flux by both eddy correlation and conditional sampling. Field tests were conducted in a soybean (Glycine max) field at Rosemount, MN. Two data sets of sensible heat flux, each with more than 250 observations, both produced estimates of β = 0.56, comparable with the previously reported estimate of 0.6 ± 10%. We show that this is not an altogether empirical value, but is derivable from statistical considerations. The value of 0.56 was used in subsequent measurements of latent heat flux (LE) and carbon exchange rate (CER) with an IR gas analysis-based system. There are discretization errors in the sample system that caused systematic underestimation of ΔC, for which we derived an approximate correction. Even with this correction, measurements of LE still tended to be somewhat lower than residual energy balance estimates of LE. In one 4 day trial, the slope of measured LE vs. residual-estimated LE was 0.96, whereas in a second trial the slope was only 0.86. Although such results indicate that further research is necessary, they also provide encouragement that such work will be worthwhile.

Reicosky, D.C., Winkelman, L.J., Baker, J.M. and Baker, D.G., 1989. Accuracy of hourly air temperatures calculated from daily minima and maxima. Agric. For. Meteorol., 46: 193-209. Temperature is one of the critical variables that drives biological systems and is of fundamental importance in crop growth models. The objective of this work was to determine the accuracy of several methods for calculating hourly air temperatures from daily maxima and minima. Methods that have as inputs daily minimum and maximum temperature were selected from the literature based on their use in existing growth models and simplicity. Four years of hourly air temperature data collected during the growing season at 2 m over well-watered grass were used to test the various methods. Six days from each growing season were randomly selected for detailed analysis, and an additional 9 days were selected to cover a range of daily maximum temperatures and solar radiation. The absolute mean error within a 24-h period ranged from 0.5 to 9.3 ° C for the 6 randomly selected days for all 4 years of the data. All methods worked reasonably well on clear days but with limited success on overcast days. Daily maximum temperature did not appear to affect the accuracy of any of the methods. If accurate timing of temperature input to models is critical, the results indicate direct measurement of hourly temperature may be necessary.

An axisymmetric finite element model of heat flow was used to evaluate some of the assumptions inherent in the steady-state heat balance method for measuring sap flow in herbaceous plants. Results indicate that the gauge slightly overestimates conduction up and down the stem when sap flow is nearly zero, causing a corresponding underestimate of the sheath conductance and the radial outward heat flux. As sap flow rates increase, the temperature distribution in the stem and gauge is altered to the point that the one-dimensional Fourier equations are no longer applicable and the individual heat fluxes in the system are poorly estimated. However, the errors are largely self-compensating, so that the resulting gauge estimate of the heat absorbed by the sap stream is reasonably accurate. The model indicates that stem vascular anatomy affects the accuracy of the method, predicting that, in general, the method should be more accurate with dicots than with monocots.