Planting Date, Hybrid Maturity, and Weather Effects on Maize Yield and Crop Stage

Unfavorable weather conditions frequently cause farmers to plant maize (Zea mays L.) outside the optimum planting timeframe. We analyzed maize yield and phenology from a multilocation, year, hybrid relative maturity, and planting date experiment performed in Iowa, USA. Our objectives were to determine the optimum combination of planting date and relative maturity to maximize maize grain yield per environment and to elucidate the risk associated with the use of “full-season hybrids” when planting occurs beyond the optimum planting date. Analysis of variance (ANOVA) attributed 70% of the variability in grain yield to planting date and only 10% to relative maturity indicating that short and full-season hybrid relative maturities produced similar grain yields regardless of when they were planted as long as the crops reached maturity before harvesting. Our analysis indicated time to silking is a good indication of expected yield potential with a critical time (beyond which yield is reduced) to be 23 July for Iowa. Furthermore, we found that a minimum growing degree accumulation of 648°Cday during the grain-filling period maximized maize yield. Overall, this study brings new results to assist decision making regarding planting date by hybrid relative maturity across Iowa

Plant litter quality affects the accumulation rate, composition, and stability of mineral-associated soil organic matter

Mineral-associated organic matter (MAOM) is a relatively large and stable fraction of soil organic matter (SOM). Plant litters with high rates of mineralization (high quality litters) are hypothesized to promote the accumulation of MAOM with greater efficiency than plant litters with low rates of mineralization (low-quality litters) because litters with high rates of mineralization maximize the synthesis of microbial products and most MAOM is microbial-derived. However, the effect of litter quality on MAOM is inconsistent. We conducted four repeated short-term incubations (46-d each) of four plant litters (alfalfa, oats, maize and soybean) in two low-carbon subsoils (sandy loam and silty loam) with and without nutrient addition. Our short-term incubations focused on the initial stage of litter decompositionduring the time when litter quality has a measureable effect on mineralization rates. Plant litter quality had a much greater effect on litter-C mineralization rate and MAOM-C accumulation than did soil type or nutrient addition. Soils amended with high-quality oat and alfalfa litters had greater MAOM-C accumulation than soils amended with low-quality maize and soybean litters. However, soils amended with high-quality litters also had greater litter-C mineralization than soils amended with low-quality litters. As a result, the accumulation of MAOM-C per unit of litter-C mineralization was lower in soils amended with high-vs. low-quality litters (0.65 vs. 1.39 g MAOM-C accumulated g−1 C mineralized). Cellulose and hemicelluose indices of accumulated MAOM were greater for maize and soybean than oats and alfalfa, however, most carbohydrates in MAOM were plant-derived regardless of litter quality. At the end of the incubations, more of the accumulated MAOM-N was potentially mineralizable in soils amended with high quality litters. Nevertheless, most of the litter-C remained as residual litter; just 12% was mineralized to CO2 and 13% was transferred to MAOM. Our results demonstrate several unexpected effects of litter quality on MAOM stabilization including the direct stabilization of plant-derived carbohydrates

Maize Leaf Appearance Rates: A Synthesis From the United States Corn Belt

The relationship between collared leaf number and growing degree days (GDD) is crucial for predicting maize phenology. Biophysical crop models convert GDD accumulation to leaf numbers by using a constant parameter termed phyllochron (°C-day leaf−1) or leaf appearance rate (LAR; leaf oC-day−1). However, such important parameter values are rarely estimated for modern maize hybrids. To fill this gap, we sourced and analyzed experimental datasets from the United States Corn Belt with the objective to (i) determine phyllochron values for two types of models: linear (1-parameter) and bilinear (3-parameters; phase I and II phyllochron, and transition point) and (ii) explore whether environmental factors such as photoperiod and radiation, and physiological variables such as plant growth rate can explain variability in phyllochron and improve predictability of maize phenology. The datasets included different locations (latitudes between 48° N and 41° N), years (2009–2019), hybrids, and management settings. Results indicated that the bilinear model represented the leaf number vs. GDD relationship more accurately than the linear model (R2 = 0.99 vs. 0.95, n = 4,694). Across datasets, first phase phyllochron, transition leaf number, and second phase phyllochron averaged 57.9 ± 7.5°C-day, 9.8 ± 1.2 leaves, and 30.9 ± 5.7°C-day, respectively. Correlation analysis revealed that radiation from the V3 to the V9 developmental stages had a positive relationship with phyllochron (r = 0.69), while photoperiod was positively related to days to flowering or total leaf number (r = 0.89). Additionally, a positive nonlinear relationship between maize LAR and plant growth rate was found. Present findings provide important parameter values for calibration and optimization of maize crop models in the United States Corn Belt, as well as new insights to enhance mechanisms in crop models

Leaf photosynthesis and respiration of three bioenergy crops in relation to temperature and leaf nitrogen: how conserved are biochemical model parameters among crop species?

Given the need for parallel increases in food and energy production from crops in the context of global change, crop simulation models and data sets to feed these models with photosynthesis and respiration parameters are increasingly important. This study provides information on photosynthesis and respiration for three energy crops (sunflower, kenaf, and cynara), reviews relevant information for five other crops (wheat, barley, cotton, tobacco, and grape), and assesses how conserved photosynthesis parameters are among crops. Using large data sets and optimization techniques, the C3 leaf photosynthesis model of Farquhar, von Caemmerer, and Berry (FvCB) and an empirical night respiration model for tested energy crops accounting for effects of temperature and leaf nitrogen were parameterized. Instead of the common approach of using information on net photosynthesis response to CO2 at the stomatal cavity (An–Ci), the model was parameterized by analysing the photosynthesis response to incident light intensity (An–Iinc). Convincing evidence is provided that the maximum Rubisco carboxylation rate or the maximum electron transport rate was very similar whether derived from An–Ci or from An–Iinc data sets. Parameters characterizing Rubisco limitation, electron transport limitation, the degree to which light inhibits leaf respiration, night respiration, and the minimum leaf nitrogen required for photosynthesis were then determined. Model predictions were validated against independent sets. Only a few FvCB parameters were conserved among crop species, thus species-specific FvCB model parameters are needed for crop modelling. Therefore, information from readily available but underexplored An–Iinc data should be re-analysed, thereby expanding the potential of combining classical photosynthetic data and the biochemical model

Consistent negative response of US crops to high temperatures in observations and crop models

High temperatures are detrimental to crop yields and could lead to global warming-driven reductions in agricultural productivity. To assess future threats, the majority of studies used process-based crop models, but their ability to represent effects of high temperature has been questioned. Here we show that an ensemble of nine crop models reproduces the observed average temperature responses of US maize, soybean and wheat yields. Each day >30 °C diminishes maize and soybean yields by up to 6% under rainfed conditions. Declines observed in irrigated areas, or simulated assuming full irrigation, are weak. This supports the hypothesis that water stress induced by high temperatures causes the decline. For wheat a negative response to high temperature is neither observed nor simulated under historical conditions, since critical temperatures are rarely exceeded during the growing season. In the future, yields are modelled to decline for all three crops at temperatures >30 °C. Elevated CO 2 can only weakly reduce these yield losses, in contrast to irrigation

Maize and soybean root front velocity and maximum depth in Iowa, USA

Quantitative measurements of root traits can improve our understanding of how crops respond to soil and weather conditions, but such data are rare. Our objective was to quantify maximum root depth and root front velocity (RFV) for maize (Zea mays) and soybean (Glycine max) crops across a range of growing conditions in the Midwest USA. Two sets of root measurements were taken every 10–15 days: in the crop row (in-row) and between two crop rows (center-row) across six Iowa sites having different management practices such as planting dates and drainage systems, totaling 20 replicated experimental treatments. Temporal root data were best described by linear segmental functions. Maize RFV was 0.62 ± 0.2 cm d−1 until the 5th leaf stage when it increased to 3.12 ± 0.03 cm d−1 until maximum depth occurred at the 18th leaf stage (860 °Cd after planting). Similar to maize, soybean RFV was 1.19 ± 0.4 cm d−1 until the 3rd node when it increased to 3.31 ± 0.5 cm d−1 until maximum root depth occurred at the 13th node (813.6 °C d after planting). The maximum root depth was similar between crops (P \u3e 0.05) and ranged from 120 to 157 cm across 18 experimental treatments, and 89–90 cm in two experimental treatments. Root depth did not exceed the average water table (two weeks prior to start grain filling) and there was a significant relationship between maximum root depth and water table depth (R2 = 0.61; P = 0.001). Current models of root dynamics rely on temperature as the main control on root growth; our results provide strong support for this relationship (R2 \u3e 0.76; P \u3c 0.001), but suggest that water table depth should also be considered, particularly in conditions such as the Midwest USA where excess water routinely limits crop production. These results can assist crop model calibration and improvements as well as agronomic assessments and plant breeding efforts in this region

Maize and soybean root front velocity and maximum depth in Iowa, USA

Quantitative measurements of root traits can improve our understanding of how crops respond to soil and weather conditions, but such data are rare. Our objective was to quantify maximum root depth and root front velocity (RFV) for maize (Zea mays) and soybean (Glycine max) crops across a range of growing conditions in the Midwest USA. Two sets of root measurements were taken every 10–15 days: in the crop row (in-row) and between two crop rows (center-row) across six Iowa sites having different management practices such as planting dates and drainage systems, totaling 20 replicated experimental treatments. Temporal root data were best described by linear segmental functions. Maize RFV was 0.62 ± 0.2 cm d−1 until the 5th leaf stage when it increased to 3.12 ± 0.03 cm d−1 until maximum depth occurred at the 18th leaf stage (860 °Cd after planting). Similar to maize, soybean RFV was 1.19 ± 0.4 cm d−1 until the 3rd node when it increased to 3.31 ± 0.5 cm d−1 until maximum root depth occurred at the 13th node (813.6 °C d after planting). The maximum root depth was similar between crops (P \u3e 0.05) and ranged from 120 to 157 cm across 18 experimental treatments, and 89–90 cm in two experimental treatments. Root depth did not exceed the average water table (two weeks prior to start grain filling) and there was a significant relationship between maximum root depth and water table depth (R2 = 0.61; P = 0.001). Current models of root dynamics rely on temperature as the main control on root growth; our results provide strong support for this relationship (R2 \u3e 0.76; P \u3c 0.001), but suggest that water table depth should also be considered, particularly in conditions such as the Midwest USA where excess water routinely limits crop production. These results can assist crop model calibration and improvements as well as agronomic assessments and plant breeding efforts in this region

CGIAR modeling approaches for resource constrained scenarios: IV Models for analyzing socio‐economic factors to improve policy recommendations

International crop-related research as conducted by the CGIAR uses crop modelingfor a variety of purposes. By linking crop models with economic models andapproaches, crop model outputs can be effectively used as inputs into socioeco-nomic modeling efforts for priority setting and policy advice using ex-ante impactassessment of technologies and scenario analysis. This requires interdisciplinarycollaboration and very often collaboration across a variety of research organizations.This study highlights the key topics, purposes, and approaches of socioeconomicanalysis within the CGIAR related to cropping systems. Although each CGIARcenter has a different mission, all CGIAR centers share a common strategy of strivingtoward a world free of hunger, poverty, and environmental degradation. This meansresearch is mostly focused toward resource-constrained smallholder farmers. Thereview covers global modeling efforts using the IMPACT model to farm householdbio-economic models for assessing the potential impact of new technologies onfarming systems and livelihoods. Although the CGIAR addresses all aspects of foodsystems, the focus of this review is on crop commodities and the economic analysislinked to crop-growth model results. This study, while not a comprehensive review,provides insights into the richness of the socioeconomic modeling endeavors withinthe CGIAR. The study highlights the need for interdisciplinary approaches to addressthe challenges this type of modeling faces