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

Kenaf Productivity and Morphology. When Grown in Iowa and in Kentucky. Poster Number

Natural fibers are a promising alternative to synthetic fibers for reinforcing plastic or other composite materials, or fuel purposes. Kenaf (Hibiscus cannabinus L.), a fiber crop is grown to a limited extent in the U.S. predominantly in the Southern states. Producing kenaf in the Midwestern U.S. could provide a local source of these fibers for use in a number of manufactured products and potentially for use as a biofuel feedstock. The objectives of this study were to: 1) compare the productivity and the morphology of kenaf cultivars ‘Tainung 2’ and ‘Whitten’ when grown in Iowa and Kentucky and harvested after the first killing frost; 2) assess kenaf growth over the growing season; and 3) determine management (variety and seed density) effects on kenaf productivity and morphology. When grown in Kentucky in 2014, Tainung 2 yielded 24 Mg ha-1 whereas Whitten had a yield of 19 Mg ha-1. In contrast, kenaf grown in Iowa showed a yield of 8 Mg ha-1. In 2015, kenaf grown in Iowa and Kentucky had similar yield of 12.6 Mg ha-1 on average. When grown in Iowa, kenaf response to variety and seed density was more stable over time than in Kentucky. Therefore, a producer in Kentucky could influence kenaf productivity by changing management practices and variety. Tainung 2 was in general more sensitive to location and to seed density than Whitten. In 2015, Tainung 2 planted at a denser population produced 30% greater biomass than for Whitten and other seed densities. Whitten performed similarly across locations and seed densities. Growing Tainung 2 in Kentucky produced plants with 16% more core fiber than in Iowa, but using that same variety in Iowa would result in higher bast production. Overall, kenaf production is very feasible in Kentucky and Iowa but Kentucky has greater yield potential

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

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

Are soybean models ready for climate change food impact assessments?

Abstract. An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO2 concentration [CO2] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO2]. For example, under a + 3 °C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 °C temperature rise to no effect on yield. Similarly, when increasing [CO2] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble, ensemble mean yield responses to temperature and [CO2] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO2] and temperature is needed to reduce the uncertainty from the individual models

Growth and biomass productivity of kenaf (Hibiscus cannabinus, L.) under different agricultural inputs and management practices in central Greece

The growth and biomass productivity of kenaf (Hibiscus cannabinus, L.) cultivars Tainung 2 and Everglades 41 were determined under three irrigation applications (low: 25%, moderate: 50% and fully: 100% of maximum evapotranspiration; ETm), four nitrogen dressings (0, 50, 100 and 150 kg hat), two sowing dates, and two plant densities (20 and 30 pl m(-2)) in two field experiments carried out on an representative aquic soil of western Thessaly plain (central Greece), in the period 2003-2005. The results demonstrated a paramount effect of sowing time (and thus the availability of the vegetative growing period) on crop growth and biomass productivity; delayed sowings (after mid-May) may reduce biomass production by 38%. Irrigation water had a significant effect (P 0.05) on biomass accumulation. Cultivars performed similar growth rates (no significant differences), which under full water and nitrogen inputs reached maximum growth rates of 180-220 kg ha(-1) day(-1) which may serve as reference for the assessment of crop performance under production situations at hierarchically lower input and management levels for central Greek conditions. (C) 2010 Elsevier B.V. All rights reserved

Soybean profitability and yield component response to nitrogen fertilizer in Iowa

Nitrogen fertilizer application to soybean [Glycine max (L.) Merr.] in Iowa, USA, has shown inconsistent results. We performed a study in central Iowa (2015 and 2016) to investigate the effect of N fertilizer rate (0, 45, 90, 135 kg N ha−1) and application timing (planting, flowering, pod setting) on soybean yield, yield components, and to calculate the economic net return to N fertilizer. Results showed a positive effect of N fertilizer on soybean yield and yield components both years. Seed and aboveground biomass dry weight were positively correlated to N fertilizer, and both were 17% greater than No-N treatment. Nitrogen fertilizer rate that significantly increased seed and aboveground biomass was 135 kg N ha−1 regardless of application timing (2015), or at planting (2016). Moreover, the same N fertilizer addition applied at planting benefitted seed and aboveground biomass N accumulation only in 2016 (avg. 32.00 and 34.68 g N uptake m−2, respectively), both 1.5-times higher than No-N treatment. Favorable environmental conditions during 2016 lead to hand-measured yield difference of 22% compared to 2015. Economic net return analysis showed that the additional revenue from increased yield attributed to supplemental N fertilization offset the application cost, resulting in net return gains between US$5.83 to$281.89 ha−1 (all treatments except 45 kg N ha−1 on 2015). This study highlights the importance to parse out soybean yield in its components, and the need to quantify yield gains from N fertilizer additions in economic terms which shed some light on any tradeoffs.This article is published as Córdova, S. Carolina, Sotirios V. Archontoulis, and Mark A. Licht. "Soybean profitability and yield component response to nitrogen fertilizer in Iowa." Agrosystems, Geosciences & Environment 3, no. 1 (2020): e20092. doi:10.1002/agg2.20092. Posted with permission. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made