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Crop model calibration refers to the process where values are obtained for a subset of model parameters that represent cultivar traits. Calibrated parameters, however, are just as likely to compensate for limitations in model structure as well as reflect true phenotypic characteristics. This confounding of genetics with production environment limits model accuracy particularly for climate assessments and can result in crop parameter values that are location dependent. We evaluated the calibration stability of the potato (Solanum tuberosum L.) model SPUDSIM for two cultivars grown in two sites in the United States sites using a location specific (R1) and cross‐location (R2) methodology. Differences between R1 coefficient values ranged from 4 to 17% between the two sites depending on cultivar. This likely reflected over‐coupling of canopy expansion rate within the model structure as well as an over‐sensitivity to temperature and photoperiod. Broader site differences were observed for growth coefficients, particularly when compared with the R2 method. Validation year end‐of‐season yields (17% error or less) and in‐season RMSE (<36.1 g plant−1) were similar for both methods. The R2 calibration overpredicted yields by 5% or less as compared with R1, with a maximum discrepancy of 0.35 Mg ha−1 in climate forecasted impacts. Consistent differences between R1 and R2 calibrated models in rates of yield decline per decade were not observed for cultivar, location, or climate scenario, and averaged 0.034 Mg ha−1 yr−1 for R1 and 0.038 for R2. Thus, calibration for these cultivars and locations was relatively stable in SPUDSIM for the temperate climate evaluated. [ABSTRACT FROM AUTHOR]

The solar corridor concept examines approaches to better use light in row crops. To achieve this goal, an understanding of the diel patterns of light transmittance to the soil surface in the interrow zone in row crops is necessary. The objective of this study was to investigate the temporal and spatial distribution of light transmittance to the soil surface at different row positions. Light data and leaf area of maize (Zea mays L.) were collected over a period of 2 yr to quantify the spatial distribution of light transmittance to the soil surface under different plant and canopy densities. Leaf area index (LAI) was varied by using two different plant densities and a range of N applications. Photosynthetically active radiation (PAR) was measured using quantum line sensors at three to seven row positions depending on row spacing (0.78 and 0.36 m). Light transmittance to the soil surface varied by position within the row and time of day. The variation was greatest for low N and low plant density spacing treatments. On cloudy days with more diffuse light the variation in light transmittance between the within and row positions was less. Light extinction coefficients decreased as solar elevation increased toward midday. The wide variation in light transmittance to the soil surface in the interrow zone points out the need to manage plant and management properties such as leaf area, plant density, and row spacing to take best advantage of light in the solar corridor. [ABSTRACT FROM AUTHOR]

The Eastern Seaboard region (ESR) of the United States is densely populated and depends on imported food. Agricultural systems are vulnerable to uncertainties such as environmental conditions, climate change, and transportation costs. Local populations could benefit from regional food systems as a way to provide security; however, the potential production capacity of the region would first need to be quantified. Potential production capacity for a specific crop, potato (Solanum tuberosum L.), was explored in two ways: expansion of the harvested land area and closing the yield gap between observed and potential yield. Potato production was assessed from Maine to Virginia for current land use (land under potato cultivation) and potential land use (other cropland). Simulations were based on two water availability scenarios: limited and nonlimited. A geospatial model implementing the explanatory model SPUDSIM estimated crop production (crop yield, water use, and N uptake) based on spatially variable input data (weather, soil, and management). Potato production was simulated in 35 potato-producing counties and in 346 counties with cropland. Under water-limited conditions, the response surface of production showed greater yield in the northern ESR states (median 28.24Mgha-1 ) than in the southern states (median 15.41 Mg ha-1 ). Resource requirements (water and N) and biophysical constraints (climate and soil) to production were also evaluated. In general, potato yield was negatively correlated with higher average seasonal temperatures and denser soil profiles. The results from this study will be valuable for regional policy planners to assess the capacity of the regional ESR food system. [ABSTRACT FROM AUTHOR]

Mechanistic crop models capable of representing realistic temperature responses of key physiological processes are necessary for enhancing our ability to forecast crop yields and develop adaptive cropping solutions for achieving food security in a changing climate. Leaf growth and phenology are critical components of crop growth and yield that are sensitive to climate impacts. We developed a novel modeling approach that incorporates a set of nonlinear functions to augment traditional thermal time methods (e.g., growing degree days) for simulating temperature responses of leaf expansion and phenology in maize or corn (Zea mays L.). The resulting leaf expansion and phenology models have been implemented into a new crop model, MAIZSIM, that simulates crop growth based on key physiological and physical processes including C4 photosynthesis, canopy radiative transfer, C partitioning, water relations, and N dynamics for a maize plant. Coupled with a two-dimensional soil process model, 2DSOIL, MAIZSIM was applied to simulate leaf growth, phenology, biomass partitioning, and overall growth of maize plants planted at two field sites on the Eastern Shore of Maryland and in Delaware for 3 yr of data. The model parameters were estimated using data from outdoor sunlit growth chambers and the literature. No calibration was performed using the field data. The MAIZSIM model simulated leaf area, leaf addition rate, leaf numbers, biomass partitioning and accumulation with reasonable accuracy. Our study provides a feasible method for integrating nonlinear temperature relationships into crop models that use traditional thermal time approaches without sacrificing their current structure for predicting the climate change impacts on crops. [ABSTRACT FROM AUTHOR]

The degree to which crops respond to atmospheric CO2 enrichment may be influenced by nutrition. To determine the extent to which dry matter production, canopy and leaf photosynthesis, and transpiration are influenced by Ρ and CO2, potatoes (Solanum tuberosum L. cultivar Kennebec) were grown in outdoor soilplant-atmosphere research (SPAR) chambers at two levels of CO2 (400 or 800 µmol-1) and three levels of Ρ fertilization. Total dry matter declined an average 42% between high and low Ρ fertilizer and increased 13% in response to elevated CO2 when averaged across the Ρ treatments. This enhancement effect did not vary with level of Ρ treatment. Leaf level photosynthetic rate was reduced 58% and stomatal conductance 43% between high and low Ptreatments. Biochemical model parameters for carboxylation rate, ribulose bisphosphate regeneration, and triose phosphate use were reduced by Ρ deficiency but scarcely influenced by growth CO2. After tuber initiation, canopy assimilation rate increased under elevated CO2 particularly at the middle levels of Ρ fertilization, and diurnal canopy évapotranspiration showed a significant reduction in response to elevated CO2 and declining Ρ fertilizer. Lack of interactive effects between CO2 and Ρ on most measured responses suggests the effect of CO2 enrichment on potato growth and assimilation is similar at each P-treatment level; however, such effects may also be correlated with plant Ν status. [ABSTRACT FROM AUTHOR]

Field capacity or field water capacity (FC) is defined as the water content of a soil following saturation with water and after free drainage is negligible. Different recommendations exist worldwide on which, if any, pressure should be used in laboratory measurements to approximate the FC of the soil. Research has often deemed any such pressures to be inadequate to approximate FC for soils of all textures. We used a data collection from the literature to evaluate if corrections can be made to improve the estimation of FC from -33 kPa water retention (W33). Regression tree modeling coupled with jack-knife cross-validation was used to identify the best predictors—sand, silt, clay and the measured W33 value—to estimate the difference between W33 and FC. Such predictions were then used to adjust the W33 value as the estimate of FC. An improvement in estimating FC was seen in general statistical terms, and texture-specific bias was also greatly reduced. This solution may allow the reliable use of a single pressure in the laboratory to approximate FC, which may be the only feasible option for large-scale studies. [ABSTRACT FROM AUTHOR]

The effects of CO2 enrichment on the growth and physiology of maize were investigated at the molecular, biochemical, leaf, and canopy levels. Maize plants were grown in sunlit soil–plant–atmosphere research (SPAR) chambers at ambient (370 μmol mol−1) or elevated (750 μmol mol−1) atmospheric carbon dioxide concentration ( Ca) under well-watered and fertilized conditions. Canopy gas exchange rates and leaf temperatures were monitored continuously during the growing season. CO2 enrichment did not enhance the growth or canopy photosynthesis of maize plants. However, canopy evapotranspiration rates decreased by 22% and daytime leaf temperatures were increased about 1°C in response to CO2 enrichment. Leaf carboxylation efficiency and leaf nitrogen concentration also decreased at elevated Ca. Transcription profiling using maize cDNA microarrays revealed that approximately 5% of tested genes responded to CO2 enrichment. Of the altered transcripts, several were known to encode proteins involved in stomatal development or photosynthesis. For the majority of the altered transcripts, however, it was difficult to link their functions with specific physiological factors partly because many of these genes encoded unknown proteins. We conclude that maize did not exhibit enhanced growth or photosynthesis in response to CO2 enrichment but a number of molecular and physiological processes including those involved in stomatal relations were affected by growth in elevated Ca. [ABSTRACT FROM AUTHOR]

Synthesis and quantification of disciplinary knowledge at the whole system level, via the process models of agricultural systems, are critical to achieving improved and dynamic management and production systems that address the environmental concerns and global issues of the 21st century. Soil physicists have made significant contributions in this area in the past, and are uniquely capable of making the much-needed and exciting new contributions. Most of the exciting new research opportunities are trans-disciplinary, that is, lie on the interfacial boundaries of soil physics and other disciplines, especially in quantifying interactions among soil physical processes, plant and atmospheric processes, and agricultural management practices. Some important knowledge-gap and cutting-edge areas of such research are: (1) quantification and modeling the effects of various management practices (e.g., tillage, no-tillage, crop residues, and rooting patterns) on soil properties and soil—plant—atmosphere processes (2) the dynamics of soil structure, especially soil cracks and biochannels, and their effects on surface runoff of water and mass, and preferential water and chemical transport to subsurface waters; (3) biophysics of changes in properties and processes at the soil—plant and plant—atmosphere interfaces; (4) modeling contributions of agricultural soils to climate change and effects of climate change on soil environment and agriculture; and (5) physical (cause-effect) quantification of spatial variability of soil properties and their outcomes, new methods of parameterizing a variable field for field-scale modeling, and new innovative methods of aggregating output results from plots to fields to larger scales. The current status of the various aspects of these research areas is reviewed briefly. The future challenges are identified that will require both experimental research and development of new concepts, theories, and models. [ABSTRACT FROM AUTHOR]

Infiltration and water flow in soils are highly transient processes, but may be estimated from high frequency measurements of soil water content. The objectives of our study were to assess the impact of vadose zone soil water dynamics from real-time and near-continuous soil water content measurements and to assess the interactive effects of tillage, row position, and season on water infiltration, storage, drainage, and crop water uptake. Multisensor capacitance probes were installed at row and interrow positions at four soil depths in plow tillage (PT) and no tillage (NT) corn (Zea mays L.) plots, with volumetric water contents values recorded every 10 min for a 30-mo period. Three water parameters (cumulative water storage, net water storage, and drainage below 55 cm) were grouped for statistical analysis by rainfall amounts and "seasons" of primary periods of groundwater recharge and evapotranspiration. Crop water uptake amounts during extended dry-down periods were also quantified. We found more significant differences between tillage and row position treatments for high rainfalls (>17 mm) than for low rainfalls. When significant differences in the three water parameters existed between positions, these were due either to greater values in the row position or smaller values at the traffic interrow position. In general, more water uptake took place under PT than under NT and from row more than interrow or traffic interrow positions. These results show both the value and limitation of near-continuous real-time soil water data for quantifying soil water dynamics in varying management and weather conditions. [ABSTRACT FROM AUTHOR]