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Fractional green canopy cover (FGCC) is a key diagnostic variable that can be used to estimate canopy development, light interception, and evapotranspiration partitioning. Available image analysis tools for quantifying FGCC are time-consuming or expensive and cannot analyze video. Our objective was to develop a simple, accurate, and rapid tool to analyze FGCC from images and videos. Th is tool, called Canopeo, was developed using Matlab and is based on color ratios of red to green (R/G) and blue to green (B/G) and an excess green index (2G-R-B). Th e output from this tool was compared to that from two soft ware packages widely used to analyze FGCC, SamplePoint, and SigmaScan Pro. Canopeo's image processing speed was 20 to 130 times faster than SigmaScan and 75 to 2500 times faster than SamplePoint. Canopeo correctly classifi ed 90% of pixels when compared to SamplePoint. Root mean squared diff erence

The soil thermal properties—heat capacity (C), thermal diffusivity (α), and thermal conductivity (λ)—are important in many agricultural, engineering, and meteorological applications. Soil thermal properties are largely dependent on the volume fraction of water (θ), volume fraction of solids (vs), and volume fraction of air (na) in the soil. In many natural settings θ, vs, and na vary greatly over time and space, but data showing the effects of these variations on thermal properties are not readily available. We used a heat‐pulse method to measure the thermal properties of 59 packed columns of four medium‐textured soils covering large ranges of θ, vs, and na The measured data reveal the commonly overlooked but dominant influence of na on soil thermal properties. Notably, the measurements show that the λ of these soils at 20°C can be accurately described as a decreasing linear function of na Good agreement exists between the measured data and common models for λ and C

A winter rye (Secale cereale L.) cover crop can be seeded after corn (Zea mays L.) silage to mitigate some of the environmental concerns associated with corn silage production. Rye can be managed as a cover crop by chemical termination or harvested for forage. A field study was conducted in Morris, MN in 2008 and 2009 to determine the impact of killed vs. harvested rye cover crops on soil moisture and NO3–N, and to monitor the impact of the rye on subsequent corn yield. Corn for silage was seeded either after winter fallow (control), after a rye cover crop terminated 3 to 4 wk before corn planting (killed rye), or after a rye forage crop harvested no more than 2 d before corn planting (harvested rye). Soil moisture after killed rye was similar to the control, but after harvested rye was 16% lower. Available soil NO3–N was decreased after both killed rye (35%) and harvested rye (59%) compared to the control. Corn biomass yield after killed rye was similar to the control, but yield following harvested rye was reduced by 4.5 Mg ha−1 Total forage biomass yield (silage + rye) was similar for all treatments. This work demonstrates that the environmental benefits of a winter rye cover crop can be achieved without impacting corn yield, but the later termination required for rye forage production resulted in soil resource depletion and negatively impacted corn silage yield.

Soil moisture patterns are commonly thought to be dominated by land surface characteristics, such as soil texture, at small scales and by atmospheric processes, such as precipitation, at larger scales. However, a growing body of evidence challenges this conceptual model. We investigated the structural similarity and spatial correlations between mesoscale (∼1–100 km) soil moisture patterns and land surface and atmospheric factors along a 150 km transect using 4 km multisensor precipitation data and a cosmic‐ray neutron rover, with a 400 m diameter footprint. The rover was used to measure soil moisture along the transect 18 times over 13 months. Spatial structures of soil moisture, soil texture (sand content), and antecedent precipitation index (API) were characterized using autocorrelation functions and fitted with exponential models. Relative importance of land surface characteristics and atmospheric processes were compared using correlation coefficients (r) between soil moisture and sand content or API. The correlation lengths of soil moisture, sand content, and API ranged from 12–32 km, 13–20 km, and 14–45 km, respectively. Soil moisture was more strongly correlated with sand content (r = −0.536 to −0.704) than with API for all but one date. Thus, land surface characteristics exhibit coherent spatial patterns at scales up to 20 km, and those patterns often exert a stronger influence than do precipitation patterns on mesoscale spatial patterns of soil moisture.

Soil moisture data from the Oklahoma Mesonet are widely used in research efforts spanning many disciplines within Earth sciences. These soil moisture estimates are derived by translating measurements of matric potential into volumetric water content through site- and depth-specific water retention curves. The objective of this research was to increase the accuracy of the Oklahoma Mesonet soil moisture data through improved estimates of the water retention curve parameters. A comprehensive field sampling and laboratory measurement effort was conducted that resulted in new measurements of the percent of sand, silt, and clay; bulk density; and volumetric water content at −33 and −1500 kPa. These inputs were provided to the Rosetta pedotransfer function, and parameters for the water retention curve and hydraulic conductivity functions were obtained. The resulting soil property database, MesoSoil, includes 13 soil physical properties for 545 individual soil layers across 117 Oklahoma Mesonet sites. The root-mean-square difference (RMSD) between the resulting soil moisture estimates and those obtained by direct sampling was reduced from 0.078 to 0.053 cm3 cm−3 by use of the new water retention curve parameters, a 32% improvement. A >0.15 cm3 cm−3 high bias on the dry end was also largely eliminated by using the new parameters. Reanalysis of prior studies that used Oklahoma Mesonet soil moisture data may be warranted given these improvements. No other large-scale soil moisture monitoring network has a comparable published soil property database or has undergone such comprehensive in situ validation.

Since 1980, average wheat (Triticum aestivum L.) yields have remained nearly stagnant in the southern Great Plains (SGP) and stagnant in the state of Oklahoma. Yield stagnation can sometimes be attributed to a relatively small gap between current and potential yields, but the magnitude of the yield gap for this region has not been well quantified. The objective of this study was to determine the wheat yield and production gaps in Oklahoma at state and county levels. This involved estimation of attainable yield (Y a ) using a frontier yield function and water-limited potential yield (Y p ) using estimated transpiration and transpiration efficiency. Yield gap and production gap relative to Y a and Y p were calculated using grain yields and harvested area for 19 counties. Current average yield (Y c ) was 2.06 Mg ha -1 at the state level, well below the maximum recorded yield at the plot level of 6.59 Mg ha -1 . The Y p of current wheat varieties is far above Y c in Oklahoma, and Y c represents 74% of Y a but only 30% of Y p at state level. For growing season rainfall (GSRF) amount <250 mm wheat yields were often water-limited. However, average GSRF was 471 mm, and yield was typically limited by factors other than GSRF amount. Production exhibited greater temporal variability than yield, and production gap may be a better indicator than yield gap for regions with highest potential to increase production. Low yields and yield stagnation in Oklahoma cannot be attributed to a small remaining yield gap, nor to inadequate GSRF amount.