Physiological Maturity Dates Research Articles

Using genomic prediction with crop growth models enables the prediction of associated traits in wheat.

Crop growth models (CGM) can predict the performance of a cultivar in untested environments by sampling genotype-specific parameters. As they cannot predict the performance of new cultivars, it has been proposed to integrate CGMs with whole genome prediction (WGP) to combine the benefits of both models. Here, we used a CGM-WGP model to predict the performance of new wheat (Triticum aestivum) genotypes. The CGM was designed to predict phenology, nitrogen, and biomass traits. The CGM-WGP model simulated more heritable GSPs compared with the CGM and gave smaller errors for the observed phenotypes. The WGP model performed better when predicting yield, grain number, and grain protein content, but showed comparable performance to the CGM-WGP model for heading and physiological maturity dates. However, the CGM-WGP model was able to predict unobserved traits (for which there were no phenotypic records in the reference population). The CGM-WGP model also showed superior performance when predicting unrelated individuals that clustered separately from the reference population. Our results demonstrate new advantages for CGM-WGP modelling and suggest future efforts should focus on calibrating CGM-WGP models using high-throughput phenotypic measures that are cheaper and less laborious to collect.

Temperature-based prediction of harvest date in winter and spring cereals as a basis for assessing viability for growing cover crops

• The growth and viability of cover crops depends on autumn growth duration. • The viability of growing cover crops was assessed with a phenology model. • The phenology model predicts harvest time of spring and winter cereals. • The harvest time varies more spatially across Denmark than between years. • Simulations show that cover crops can be reliably established in southern Denmark. Cover crops are an important component of sustainable cereal cropping systems by providing nutrient retention and improving soil quality. However, the successful establishment and growth of cover crops depend on a timely harvest of the preceding annual crops. To assess the viability of growing cover crops in Denmark, a phenology model was developed, calibrated, cross-validated and applied to predict the harvest date of spring barley and winter wheat in Denmark using data from field experiments in Denmark during 1991 to 2018. A simple phenology model was used for the period from sowing to maturity, and this was extended to simulate the duration from maturity to harvest. This extended model was used to predict the harvest date in the ongoing season, and it provides the capability for assessing the performance of cover crops as a basis for improved cover crop management. The model uses temperature sums and day length to calculate the developmental rate of spring barley from sowing to harvest and for winter wheat from 1st January to harvest. The standard deviation of the estimated harvest date based on the uncertainty of the model parameters was 10-14 days and 1-4 days for spring barley and winter wheat, respectively. The model was evaluated using historically recorded temperature data (20 years, 1999–2018) to simulate harvest date, which was within the range of observed harvest dates. The interannual range of estimated harvest dates is 7–10 days for spring barley and 5–10 days for winter wheat. This interannual variation of harvest date was lower than the spatial variation across the country. The model can be used for forecasting cereal harvest time and thus readiness for autumn field operations, including the establishment of cover crops. Simulations of growing degree days from date of harvest to 1 November shows that cover crops can be reliably established in southern parts of Denmark to ensure low nitrate leaching. Advancing the harvest date to the date of physiological maturity of the cereals would allow the establishment of efficient cover crops in all years in the entire country.

Impact and Control of Powdery Mildew on Irrigated Soybean Varieties Grown in Southeast Australia

Powdery mildew—caused by the fungus Erisyphe diffusa (syn. Microsphaera diffusa)—was first observed in commercial soybean crops in southern New South Wales (NSW), Australia, in 2011. Its detection raised concerns that soybean production might be constrained if the severity of the disease reached the levels observed in northern Australia. Field experiments were conducted over four consecutive seasons to examine the response of three soybean cultivars—Djakal, SnowyA and the breeding line N005A-80—to two fungicides and two fungicide application regimes. The cultivar Djakal was identified as having a high level of resistance to powdery mildew. The severity of infection symptoms varied between seasons. The most severe symptoms were observed during the 2014–2015 season which resulted in the largest grain yield reduction of 20% for the cultivar SnowyA. All fungicide treatments provided a significant reduction in the severity of symptoms, with the split application of tebuconazole and both the single and split applications of tebuconazole + prothioconazole providing the most effective control of the disease. Few other grain yield effects were found, even when strong disease control was achieved. This was a suspected result of the consistent late-in-the-season onset of the disease. Few differences were observed among the treatments in terms of lodging severity, date of physiological maturity, or grain oil and protein concentrations. It was concluded that both fungicides provided effective control of powdery mildew. However, when disease pressure is low, application might not be warranted in southern NSW.

Quantification of the impacts of climate warming and crop management on canola phenology in Punjab, Pakistan

AbstractYield is influenced by the length of the growing season, which is affected by weather conditions and management practices of a crop, including sowing dates and shifting of cultivars. It is necessary to understand the effects of agronomic management practices and weather variables on phenological stages and crop phases in order to develop strategies for adaptation of agricultural systems to changes in climatic conditions. The goal of this study was to determine the impact of warming trends on phenology of canola from 1980 to 2014 for central and southern Punjab, Pakistan. Sowing, emergence, anthesis and physiological maturity dates were delayed by an average of 6.02, 3.14, 3.31 and 1.89 days per decade, respectively. The duration of sowing to anthesis, sowing to physiological maturity and anthesis to physiological maturity phases decreased an average 2.71, 4.13 and 1.42 days per decade, respectively, for all 10 locations that were analysed in this study. The sowing, emergence, anthesis and physiological maturity dates were positively correlated with an increase in temperature by an average 2.71, 1.41, 1.49 and 0.85 days per °C, respectively. However, the phenological phases such as sowing to anthesis, anthesis to maturity and sowing to maturity were negatively correlated with an increase in temperature by an average of 1.22, 0.64 and 1.86 days per °C, respectively, for all 10 locations. Applying a process‐based CSM‐CROPGRO‐Canola model using a standard cultivar (field tested) for all locations and years indicated that the simulated phenological stages occurred earlier due to the warming trend compared to the observed phenological stages. One‐quarter of the negative effects of this thermal trend was compensated by growing new cultivars that had higher thermal time requirements. Therefore, new canola genotypes with a higher number of growing degree day requirement and high temperature tolerance should be a priority for evolving new cultivars.

RESPONSE OF SOME WHEAT CULTIVARS TO SOME ORGANIC AND MINERAL NITROGEN FERTILIZER LEVELS

This study was conducted to examine the effect of five organic and mineral nitrogen fertilizer levels (F1 = 0, F2 = 17.5 kg organic nitrogen (ON) + 17.5 kg mineral nitrogen (MN), F3 = 35 kg MN, F4 = 35 kg ON + 35 kg MN and F5 = 70 kg MN / fed) on the growth characters, yield, its component and some chemical properties of three bread wheat cultivars (Sids 12, Gemmeiza 11 and Giza 168).The experimentwas carried out at two locations, the first location, at the Research and Experimental Centerof Faculty of Agriculture of Moshtohor,(Toukh Directorate, El-Kalubia Governorate) Benha University, Egypt, the second location, at the farm of Zarzora Agricultural Research Station, Etay El-Baroud Directorate, El-Behaira Governorate, Egypt, which planting in the same winter season 2014/2015. The obvious results of this investigation can be summarized as follows: Wheat cultivars were significantly differed in all traits studied in the two locations and the combinedaverage, except nitrogen use efficiency was not significant. Gemmeiza 11 cultivar significantly surpassed Sids 12 and Giza 168 cultivars in flag leaf area (cm2), plant height (cm), spike length (cm), spike weight (g), kernels weight/spike (g), 1000-kernel weight (g), biological yield (kg/fed), grain yield (kg/fed) and grain protein yield (kg/fed) as well as grain nitrogen uptake (kg/fed). While,Sids 12 recorded the highest values of No. of kernels/spike, harvest index (%), hectoliter weight (kg/hl), grain nitrogen content (%) and protein content in grain (%) also, Sids 12 cultivar was earlier heading and physiological maturity dates compared with Giza 168 and Gemmeiza 11. Meanwhile, Giza 168 surpassed the other two cultivars in No. of tillers/m2, No. of spike/m2 and straw yield (kg/fed). All characters under study showed significantly increased by increasing nitrogen fertilizer levels,except NUE significantly decreased with increasing N levels in Moshtohor location, Zarzora locationandthe combined analysis.Applied of 70 kg MN /fed recorded the highest values, also increasing nitrogen fertilizer levels delayed heading and physiological maturity dates. Planting Gemmeiza 11 which fertilized by 70 kg MN/fedrecorded significantly the highest values of flag leaf area, biological, grain and protein yields/fed as well as grain nitrogen uptake/fed(in the two locations and the combined analysis), plant height and weight of kernels/spike (in Moshtohor location only and the combined analysis). Meanwhile, Sids 12 under the same nitrogen fertilizer surpassed the other treatments in No. of kernels/spike (in Moshtohor location only and the combined analysis) and harvest index (in the two locations and the combined average). Whereas, Giza 168 with the same level of nitrogen fertilizer gave the highest No. of tillers/m2 (in Moshtohor location only and the combined analysis), No. of spike/m2 and straw yield/fed (in the two locations and the combined analysis). It could be summarized that sown wheat Gemmeiza 11 cultivar and application of 70 kg mineral nitrogen per fed or 35 kg organic N + 35 kg mineral nitrogen maximized grain yield per unit area.

Optimizing Wheat Water Productivity as Affected by Irrigation and Fertilizer-nitrogen Regimes in an Arid Environment

The present study evaluated the applicability of the CropSyst model under variable climatic, irrigation, and fertilizer-nitrogen regimes. The objective was to analyze wheat productivity responses to water and N-application for optimizing water productivity in an arid irrigated environment. Evaluation analysis showed that the model provided very satisfactory estimates for the emergence, flowering and physiological maturity dates. The performance of the model was reasonable as demonstrated by the close correspondence between simulated grain yield, biomass accumulation, seasonal ET, and irrigation water productivity (WPI) with measured data. The normalized root mean square error ranged between 5 and 10% for most of the parameters. Overall, the Willmott index of agreement between simulated and observed values of grain yield, biomass and seasonal ET were 0.99, 0.98 and 0.97, respectively. The validated model was employed to assess interactive effects of irrigation and fertilizer N on grain yield and water productivity indices. Scenario analyses indicated that WPI and WPET (ET water productivity) ranged from 0.16 to 2.07 kg m -3 , and from 0.07 to 1.49 kg m -3 , respectively. For predicting the best N and water application practices for maximization of water productivity, the best option found by the model was application of water and nitrogenous fertilizer in 70% and 90% of the required values, respectively, for WPI, and equal to the required values (100%) for WPET. The simulations demonstrated that the current wheat productivity of 5.0 Mg ha -1 obtained by the local farmers can be achieved at 140 kg ha -1 fertilizer N and 30% deficit irrigation regime with a WPI of 1.73 kg m -3 . The CropSyst model can be applied to derive best management options in terms of N and irrigation application of wheat under arid conditions.

Eficiência do modelo Ceres-Maize na simulação do desempenho de híbridos de milho

O modelo Ceres-Maize foi desenvolvido para simulação do desenvolvimento e desempenho da cultura do milho e tem sido utilizado como ferramenta de auxílio no planejamento das safras e tomadas de decisões pelos agricultores de diversos países. Com o objetivo de avaliar a eficiência do modelo Ceres-Maize na simulação do desempenho de híbridos de milho nas condições tropicais, foi conduzido um experimento utilizando cinco híbridos (AG7000, AG8060, DKB199, GNZ2004 e P30F90) avaliados em três épocas de semeadura (24/11/2006, 19/12/2006 e 13/01/2007) na Universidade Federal de Lavras, Lavras, MG. O delineamento foi o DBC com três repetições. Avaliaram-se datas de florescimento e maturidade fisiológica, número de grãos por metro quadrado, massa de grãos e produtividade de grãos, que foram comparados com os dados simulados pelo quadrado médio do erro (RSME), porcentagem de desvio (PD) e índice de concordância (d). Os resultados indicaram que o milho semeado em janeiro apresentou menores valores de número de grãos por metro quadrado, massa de grão e produtividade de grãos do que semeaduras em novembro e dezembro. O Ceres-Maize mostrou-se muito eficiente para simular as datas de florescimento e de maturidade fisiológica em razão dos valores de RSME terem sido inferiores a 10%, os de 'd' superiores a 0,80 e o maior valor de PD -11%. Para o número de grãos por metro quadrado, massa de grãos e produtividade de grãos, a simulação foi considerada boa com valores de RSME inferiores a 20%. Para essas variáveis foram observados maiores valores de PD, principalmente na última época de semeadura, evidenciando que condições ambientais não favoráveis ao bom desempenho da cultura afetam a eficiência da simulação. O modelo Ceres-Maize mostrou ser boa ferramenta de simulação das características agronômicas de híbridos de milho.

Estimating Genetic Coefficients for the CSM‐CERES‐Maize Model in North Carolina Environments

CSM‐CERES‐Maize has been extensively used worldwide to simulate corn growth and grain production, but has not been evaluated for use in North Carolina. The objectives of this study were to calibrate CSM‐CERES‐Maize soil parameters and genetic coefficients using official variety trial data, evaluate model performance in North Carolina, and determine the suitability of the fitting technique using variety trial data for model calibration. The study used yield data for 53 maize genotypes collected from multiple locations over a 10‐yr period. A stepwise calibration procedure was utilized: (i) two genetic coefficients which determine anthesis and physiological maturity dates were adjusted based on growing degree day requirements for each hybrid; and (ii) plant available soil water and rooting profile were adjusted iteratively with two other genetic coefficients affecting yield. Cross validation was used to evaluate the suitability of this approach for estimating soil and genetic coefficients. The root mean squared errors of prediction (RMSEPs) were similar to fitting errors. Results indicate that CSM‐CERES‐Maize can be used in North Carolina to simulate corn growth under nonlimiting N conditions and variety trial data can be used for estimating genetic coefficients. Hybrid average simulated yields matched measured yields well across a wide range of environments, and simulated hybrid yield rankings were in close agreement with rankings based on measured yields. Data from several site‐years could not be used in fitting genetic coefficients due to large root mean squared errors. In some cases, this could be attributed to a weather event, such as a late‐season hurricane.

Evaluation of CERES-Wheat and CropSyst models for water–nitrogen interactions in wheat crop

Crop simulation models can provide an alternative, less time-consuming and inexpensive means of determining the optimum crop N and irrigation requirements under varied soil and climatic conditions. In this context, two dynamic mechanistic models (CERES (Crop Environment REsource Synthesis)-Wheat and CropSyst (Cropping Systems Simulation Model)) were validated for predicting growth and yield of wheat ( Triticum aestivum L) under different nitrogen and water management conditions. Their potential as N and water management tool was evaluated for New Delhi representing semi-arid irrigated ecosystems in the Indo-Gangetic Plains. The field experiment was carried out on a silty clay loam soil at the Research Farm of the Indian Agricultural Research Institute, New Delhi, India during 2000–2001 to collect the input data for the calibration and validation of both the models on wheat crop (variety HD 2687). The models were evaluated for three water regimes [I4 (4 irrigations within the growing season), I3 (3 irrigations within the growing season) and I2 (2 irrigations within the growing season)] and five N treatments (N 0, N 60, N 90, N 120 and N 150). Both the models were calibrated using data obtained from the treatments receiving maximum nitrogen and irrigations, i.e., N 150 and I4 treatments. The models were then validated against other water and nitrogen treatments. For performance evaluation, in addition to coefficient of determination ( R 2), root mean square error (RMSE), mean absolute error (MAE) and Wilmot's index of agreement (IoA) were estimated. Both CERES-Wheat and CropSyst provided very satisfactory estimates for the emergence, flowering and physiological maturity dates. For CERES-Wheat overall prediction (pooled result of the three water regimes) of grain yield was satisfactory with significant R 2 values (0.88). The model, however, under estimated the biomass under all water regimes and N levels except for N 0 level, under which biomass was overpredicted. CropSyst predicted yield and biomass of wheat more closely than CERES-Wheat. The combined RMSE for the three water regimes between predicted and observed grain yield was 0.36 Mg ha −1 for CropSyst as compared to 0.63 Mg ha −1 for CERES-Wheat. Similarly, RMSE between observed and predicted biomass by CropSyst was 1.27 Mg ha −1 as compared to 1.94 Mg ha −1 between observed and predicted biomass by CERES-Wheat. Wilmot's index of agreement (IoA) also indicated that CropSyst model is more appropriate than CERES-Wheat in predicting growth and yield of wheat under different N and irrigation application situations in this study.

Thermal time for phenological development of four maize hybrids grown off-season in a subtropical environment

A common method to describe maize phenology during the vegetative phase is to determine the number of leaves. The thermal interval between the appearance of successive leaves, called the phyllochron, is critical for predicting the duration of vegetative development. In general, there is a high correlation between the final number of initiated leaves and the duration from sowing to silking. The development of a crop depends on temperature and photoperiod and genetic response to these environmental factors, thus bio-meteorological indices are needed to describe this process. The goal of this study was to obtain a better understanding of the phyllochron and its impact on the final number of leaves. A second objective was to evaluate three thermal indices for predicting the duration of sowing to silking and silking to physiological maturity for four maize hybrids grown off-season in a subtropical region of the Southern Hemisphere (São Paulo State, Brazil). The modified general thermal index (mGTI) was compared with the classical thermal time concept (TT) with two sets of cardinal temperatures, including TT(10) with 10 °C as minimum base temperature (Tb) and TT(8) with 8 °C as Tb. Three field experiments that included four hybrids with different relative maturity ratings were conducted during the autumn and winter growing seasons of 2001 and 2002 at the University of São Paulo, in Piracicaba, SP, Brazil. Phenology was recorded daily. Also, an independent evaluation of three methods for estimating phenology was conducted with data obtained from the variety trials of the ‘Instituto Agronômico’ for four regions of the State of São Paulo, Brazil. The results indicated that the value for the phyllochron was about 50 °Cd/leaf until approximately the appearance of the 12th leaf, and then declined. For estimating the planting to silking period, the TT(10) method resulted in a coefficient of variation (CV) of 2·01%, while for TT(8) the CV was 1·37% and for mGTI the CV was 1·04%. For the period from silking to physiological maturity, the CV was 2·91% for the TT(10), 2·64% for the TT(8) and 2·01% for the mGTI method. All CVs were very small, suggesting that there was only a slight difference between algorithms. It can be concluded from this study that the mGTI can be used for estimating both silking and physiological maturity dates for maize grown off-season in subtropical regions of Brazil because it resulted in the smallest CV. However, additional studies with different maize hybrids are necessary to confirm the advances in estimating the main phenological phases for maize based on the mGTI.

Adaptability and phenotypic stability in early common bean cultivars and lines

This study aimed at estimating the genetic variability for earliness in five common bean cultivars and nine lines. The adaptability and phenotypic stability for grain yield was also assessed using the methodology proposed by Eberhart and Russell (1966). An experiment in randomized complete blocks was carried out in three locations in the wet 2001/2002 growing season and in two locations in the 2002 dry growing season. The estimates of the coefficients of genetic variation (CVg) and environmental variation (CVe), the B index and the coefficient of genetic determination (h 2 ) showed the presence of genetic variability among the cultivars and lines assessed for the traits number of days from emergence to the appearance of the first flower (DPF) and number of days from emergence to the date of physiological maturity (DMF). The adaptability and phenotypic stability estimates showed that there was generally wide adaptability and stable performance of the cultivars and lines in the different environments. The Alubia, Baronesa and Carnaval Pitoco cultivars and the LP-20-89 and LP20-90 lines were promising, with high yield, wide adaptablity (β 1i =1) and good stability (σ 2 di =0).

Calibration and test of the cropgro-dry bean model for edaphoclimatic conditions in the savanas of Central Brazil

Simulation models are important tools for the analysis of cultivated systems to estimate the performance of crops in different environments. The CROPGRO- model (DSSAT) was calibrated and validated using Carioca bean (Phaseolus vulgaris L.) to estimate yield and the development of the crop, sown in three row spacings (0.4, 0.5, and 0.6 m) and two fertilization rates (300 and 500 kg ha-1 of 4-30-16 N-P-K), in Santo Antônio de Goiás, GO, Brazil. To calibrate the model a combination of the genetic coefficients that characterize the phenology and morphology of the dry bean crop was used to obtain the best possible fit between predicted and observed anthesis and physiological maturity dates, leaf area index (LAI), total dry matter (TDM), yield components, and grain yield for the 0.6 m row spacing. To test the model the experimental records of the 0.4 and 0.5 m row spacings were used. In both, calibration and test, the performance of the model was evaluated plotting observed and predicted values of LAI and TDM versus time, using the r², and the agreement index (d) as statistical criteria. In relation to yield and yield components the percent difference between the observed and predicted data was calculated. The model appeared to be adequate to simulate phenology, grain yield and yield components for the Carioca bean cultivar, related to different levels of fertilization and row spacing, either during calibration or the testing phase. During the test, the grain yield was overestimated by less than 15.4%, indicating a potential use for the calibrated model in assessing climatic risks in this region.

Validation of CERES-Rice v3.5 under the climate of Andhra Pradesh state, India

Crop growth simulation models, properly validated against experimental data have the potential for tactical and strategic decision making in agriculture. Such validated models can also take the information generated through site specific experiments and trials to other sites and years. For proper calibration and evaluation of crop simulation models, there is a need for collection of a comprehensive minimum set of data on soil, weather and crop management in all agronomic experiments. Keeping this in view, field experiments were conducted at Rajendranagar (17°19' N, 78°23' E; 542.3 m amsl) during 1994-97 for three popular varieties of rice viz. Sambamasuri, Rajavadlu and Tellahamsa under irrigated conditions and data collected. Genetic coefficients required for running the CERES-Rice v3.5 model were calculated and the performance of the model under the climate of the area was evaluated. The results of the study show that the model simulations of date of flowering for Sambamasuri, Rajavadlu and Tellahamsa were within an average error of 6.2, 5.7 and 6.7 days respectively. Similar errors in predictions of physiological maturity dates were 7.6, 6.7 and 7.2 days. The error in grain yield predictions by the model averaged at 7.9%, 8.3%, and 5.7% respectively for the three crop varieties. These results indicate that the CERES Rice v3.5 model is capable of prediction of grain yield and phenological development of the crop in the climatic conditions of Andhra Pradesh with reasonable accuracy and hence, the model have the potential for its use as a tool in making various strategic and tactical decisions related to agricultural planning in the state.

Modelo matemático para estimar o acúmulo de matéria seca e prever as épocas do florescimento e do ponto de maturidade fisiológica da cultura de feijão

Com o objetivo de elaborar um modelo matemático para estimar o acúmulo de matéria seca, e prever as épocas de florescimento e do ponto de maturidade fisiológica da cultura de feijão, em função da temperatura média diária do ar, foi conduzido um experimento em Piracicaba-SP (22o42’30'’ de latitude Sul, 47o38’00'’ de longitude Oeste e 546 m de altitude) utilizando o cultivar IAC-Carioca sob dez tratamentos (diferentes níveis de nitrogênio e água). Em função dos resultados obtidos, verificou-se que o modelo sugerido é utilizável para o que foi proposto.

Forecasting growth and yield of groundnut (Arachis hypogaea) with a dynamic simulation model ‘PNUTGRO’ under Punjab conditions

The dynamic simulation model ‘PNUTGRO’ was used to predict groundnut growth and yield from 1989 to 1993 at Ludhiana, India. The simulated flowering, pegging and physiological maturity dates, leaf area index (LAI), pod and seed yields and shelling percentage of groundnut were compared with actual observations for the commonly grown cultivar, M-335. The simulated phenologic events showed deviations of only −3 to +3 days for flowering, −3 to +2 days for pegging and −4 to +2 days for physiological maturity of the crop. The model estimated the LAI to be within 95–108% (mean 101·5%) and shelling percentage to be within 93–108% (mean 100·5%) of the actual values. The model predicted the pod yields from 89 to 111% (mean 100%) and seed yield from 90 to 110% (mean 100%) of the observed yields. The results obtained with the model for the five consecutive crop seasons revealed satisfactory predictions of phenology, growth and yield of groundnut and hence the model ‘PNUTGRO’ can be used for prediction of groundnut production in the central plains of the Indian Punjab.

Variation in drought susceptibility among closely related wheat lines

Drought resistance is a complex trait for which systematic improvement has been difficult. One means of simplifying analysis of such a trait is through examination of variability among closely related genotypes. Sister lines of wheat exhibiting significant variation for rainfed yield, irrigated yield, and a drought susceptibility index, S, were identified among derivatives of a backcross breeding program. Of seven sister lines compared in a 2-y field study at Bushland, Texas, three were found significantly less susceptible than a related cultivar, TAM-107, two were significantly more susceptible than TAM-107 and two were not distinguishable from TAM-107. Relatively small, but significant, variability was observed among the lines for developmental variables, including anthesis date, date of physiological maturity and length of the grain-filling period. No meaningful correlations could be established, however, between developmental variables and yield or susceptibility index. While some differences may exist among these lines in mechanism underlying relative drought susceptibility, lack of correlation with developmental variables indicates that this set of lines may be valuable in identifying useful drought resistance mechanisms in wheat.

Cultivar Maturity and Reproductive Growth Duration in Soybean

AbstractTwenty‐seven soybean (Glycine max L. (Merrill)) cultivars from maturity groups (MG) 00 through V were grown in a field at Lexington, Kentucky (38° N latitude), for 2 years, to evaluate the relationship between the duration of reproductive growth and total growth duration. Reproductive growth stages were measured at weekly intervals and dates of R1 (initial bloom) and R5 (beginning seed fill) were estimated by linear interpolation. The date of physiological maturity (growth stage R7) was based on measurements made at two day intervals. The total growth duration increased from 84 days (averaged across years) for MG 00 to 134 days for MG V. Vegetative growth (planting to R5) increased in direct proportion to the total growth cycle. The flowering and pod set period (Rl to R5) was similar for MG 00 and 0, but increased from MG I to IV. The seed filling period, estimated from R5 to R7 and by the effective filling period, increased from MG 00 to I, but there was no change from MG I through V. These data indicate that the duration of seed fill does not always increase as the total growth cycle increases.

Neural Network Models for Predicting Flowering and Physiological Maturity of Soybean

It is important for farmers to know when various plant development stages occur for making appropriate and timely crop management decisions. Although computer simulation models have been developed to simulate plant growth and development, these models have not always been very accurate in predicting plant development for a wide range of environmental conditions. The objective of this study was to develop a neural network model to predict flowering and physiological maturity for soybean (Glycine max L. Merr.). An artificial neural network is a computer software system consisting of various simple and highly interconnected processing elements similar to the neuron structure found in the human brain. A neural network model was used because it has the capabilities to identify relationships between variables of rather large and complex data bases. For this study, field-observed flowering dates for the cultivar Bragg from experimental studies conducted in Gainesville and Quincy, Florida, and Clayton, North Carolina, were used. Inputs considered for the neural network model were daily maximum and minimum air temperature, photoperiod, and days after planting or days after flowering. The data sets were split into training sets to develop the models and independent data sets to test the models. The average relative error of the test data sets for date of flowering prediction was+0.143 days (n = 21, R2 = 0.987) and for date of physiological maturity prediction was +2.19 days (n = 21, R2 = 0.950). It can be concluded from this study that the use of neural network models to predict flowering and physiological maturity dates is promising and needs to be explored further.

Selection for Early Heading and Correlated Response in Maturity of Soft Red Winter Wheat

Heading date in wheat (Triticum aestivum L. emend. Thell.) is an easily identifiable trait, and previous research has shown it can be modified. However, several studies have shown that heading date is not always highly correlated with harvest maturity. This study was conducted to gain a more thorough understanding of the relationships between heading date and traits related to maturity such as kernel growth rate (KGR), effective filling period (EFP), and time physiological maturity. We evaluated direct and correlated response to two cycles of phenotypic recurrent selection for early heading date in two wheat populations in 1989. Direct response to selection for early heading date was −5 and −10 d, respectively, in the two populations, In Population 1, correlated response to selection was not significant. We observed a significant reduction in mean KGR from 2.56 to 2.13 mg kernel−1 d−1 from the C0 to the C2 in Population 2, with no significant change in mean EFP. Heritability estimates ranged from 0.10 for yield in population 1 to 0.81 (P<0.01) for heading date in Population 2. Mean estimated date of physiological maturity (EPM) was significantly reduced in both populations after two selection cycles. Early EPM of the C2 apparently reflected an early heading date and not a reduced grain filling period. However, the earliest heading entries were not always the first to reach EPM. Thus we suggest a maturity rating based on EPM in later generations to ensure continued selection pressure for early physiological maturity.

Developmental Periods in Soybean: Photoperiod‐Sensitive ✕ Insensitive Crosses Evaluated at Diverse Latitudes

Selection for long duration of seed‐filling period (SFP) in segregating soybean [Glycine max (L.) Merr.J populations may be a way to increase seed yield. Tbe objective of tbe study was to assess the relative effectiveness of selecting for SFP from tropical environments to develop lines for temperate locations in crosses of photoperiod insensitive ✕ sensitive genotypes. A second objective was to determine if selection for longer SFP is possible without delaying date of physiological maturity (R7). Three populations were developed by crossing a photoperiod‐insensitive genotype of Maturity Group (MG) HI to photoperiod‐sensitive genotypes of MG I, II, And III. Random F4‐derived lines were evaluated at Ames, IA, at Isabela, PR (under both natural and lighted environments), and at Hualien, Taiwan, for days from emergence to beginning flowering (Rl), rapid seed‐filling (R5), R7, SFP, and reproductive period (RP). Significant (P < 0.05) differences among lines, environments, and environment ✕ genotype were observed. Phenotypic and rank correlations of Stages Rl, R5, and R7 between environments ranged from 0.22 to 0.83. The range in correlation coefficients for SFP and RP was from 0.06 to 0.62. It is possible to select lines for duration of SFP at PR environments in plantings under natural daylength or under lights. Selections in PR included genotypes ranked in the top 33% at the IA location, and generally included at least one of the five topranked lines. Lines may also be selected with a desired combination of duration of SFP and number of days to reach Stage R7.