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Soybean Flowering Date: Linear and Logistic Models Based on Temperature and Photoperiod
Crop Science
Abstract
Temperature and photoperiod are important determinants of the time from emergence to flowering in soybean [Glycine max (L.) Merr.]. A linear and a logistic model have been developed independently for describing the development rate to flowering (a high development rate means a short time to flowering). Field experiments on Arredondo fine sand soil (loamy, siliceous hyperthermic Grosarenic Paleudult) at Gainesville, FL, during 3 yr using a range of sowing dates in each year provided data to evaluate each model. In the first 2 yr, 13 cultivars were grown from 17 sowing dates (...)
... The many existing developmental models use different approaches to combine mentioned factors (Streck et al., 2003): that multiplicative models (Angus et al., 1981;Cao and Moss, 1997;Summerfield et al., 1991;Bonhomme et al., 1994;Yin et al., 1995;Yan and Wallace, 1996;Slafer and Rawson, 1996;Sinclair et al., 1991;Wang and Engel, 1998;Streck et al., 2003a,b), is one of them. The multiplicative approach appears more realistic from a biological point of view because interactions among temperature and photoperiod have been verified in field and controlled environment experiments (Streck et al., 2003;Rawson, 1994, González et al., 2002). ...
... The first model of this category was proposed by Robertson (1968). The models used by Angus et al. (1981), Sinclair et al. (1991) and Grimm et al. (1993), among others, all belong to this category, though the responses were described using different mathematical functions (quadratic, exponential or power, Angus et al., 1981). Many current crop system simulation models such as CERES and CROPSIM (Hunt and Pararajasingham, 1995) also adopted this approach. ...
... Many current crop system simulation models such as CERES and CROPSIM (Hunt and Pararajasingham, 1995) also adopted this approach. Most phenology models predicting flowering date use mean photoperiod and temperature as input variables, assuming that crop plants are sensitive to photoperiod throughout their vegetative phase from sowing to first flower (Angus et al., 1981;Roberts and Summerfield, 1987;Sinclair et al., 1991;Yan and Wallace, 1998). Developmental models also differ with respect to the nature of the response functions f(T) andf (P), from linear to several nonlinear functions (Streck et al., 2003;Ritchie, 1991;Wang and Engel, 1998;Yan and Wallace, 1998). ...
Accurate predictions of plant developmental stages are important in crop simulation models. Plant development has been measured using the number of days to flowering. The concept of flowering rate defined as the inverse of the time between emergence and flowering. It has long been recognized that photoperiod and temperature interactively modulate plant development. The multiplicative approach simulate the rate of development using a function of temperature multiplied by a function of photoperiod: R= f(T) × f(P). The relationship between temperature and photoperiod with developmental rate has been described with different equations. Our results revealed that between 24 combined models (8 equations for f(T) and 3 equations for f(P))combined model Beta-Negative exponential (B-NE) (as f(T) and f(P), respectively) has a good estimation of flowering date (or rate) in response to temperature and photoperiod. Base, optimum and ceiling temperatures as a cardinal temperatures based on B-NE were (1, 35 and 40 °C, respectively). Minimum biological required days from emergence to flowering, also, was determined as 36.85 days. Critical photoperiod and photoperiod sensitivity obtained (14.27 h and 0.37, respectively). Thermal time from emergence to flowering predicted to 1252.9. This combined model can be used for barley flowering prediction or as a sub model in other barley phonological models, although Assessment of the model using independent data and conducted several studies on other spring barley at different places are needed.
... Using iterative optimization procedures, such as simplex, there is no guarantee that the solution obtained is unique and optimal (Sinclair et al., 1991;Grimm et al., 1993;Yin et al., 1997a). There might be a series of solutions with similar SSE but different parameter estimates. ...
... Thus, it can be concluded that at least a 2-parameter model is required to account for cultivar effects and that the PS and f o parameters characterize genotypic differences in phenological development in response to temperature and photoperiod. For modeling purposes, the values of T b , T o1 , T o2 , T c and P c can be fixed as indicated in Table 4, Step 2. Grimm et al. (1993), Piper et al. (1996) and Sinclair et al. (1991) for soybean, Carberry et al. (2001) for pigeonpea and Robertson et al. (2002b) for canola also obtained good prediction of flowering time with fixed values of cardinal temperatures. However, for rice, Yin et al. (1997a) reported that optimal temperature for development could not be fixed without loss of accuracy. ...
Quantitative information on temperature and photoperiod effects on development rate in chickpea (Cicer arietinum L.) is scarce. Data from a serially sown field experiment (2001-2003) on four cultivars was used to evaluate various approaches to phenology prediction. A range of functions describing the response of development rate to temperature and photoperiod was compared. Phenological data from numerous other field experiments across Iran were used for independent model evaluation. A multiplicative model that included a dent-like function for response to temperature and quadratic function for response to photoperiod was the most adequate at describing the response of development rate to temperature and photoperiod. The differences among cultivars for cardinal temperatures and critical photoperiod were small and a base temperature of 0 8C, lower optimum temperature of 21 8C, upper optimum temperature of 32 8C, ceiling temperature of 40 8C and critical photoperiod (below which development rate decreases due to short photoperiods) of 21 h were obtained. Inherent maximum rate of development and the photoperiod sensitivity coefficient characterized cultivar differences. The cultivars required 24.7-32.2 physiological days (i.e., number of days under optimum temperature and photoperiod conditions) from emergence to flowering, 8.2-12.0 from flowering to first-pod, 4.3 from first-pod to beginning seed growth and 30.3 from beginning seed growth to maturity. Differences among cultivars were not found for first-pod to beginning seed growth or for beginning seed growth to maturity. The phenology model developed using these findings gave good predictions of phenological development for a diverse range of temperature and photoperiod conditions across Iran. This model can be incorporated in simulation models of chickpea. #
... The beginning of flowering (R1) date was simulated with the non-linear model of development response to air temperature and with photoperiod proposed by Sinclair et al. (1991), using different coefficients according to the maturity group. The occurrence date of beginning of grain filling (R5) was estimated by the model proposed by Sinclair et al. (2007), using linear regression based on photoperiod and variable coefficients according to the maturity group. ...
... There was a total reduction of 28% and 24% between the first (September 21) and the last (December 31) sowing date respectively for Santa Maria (Figs. 1A, C and E) and Pelotas (Figs. 1B, D and F). This is mainly due to the gradual increase in air temperature from September to January, increasing the daily thermal time, in addition to the greater photoperiodic induction to flowering at later sowing dates, which mainly affects the V2-R1 and R1-R5 subperiod(s), as demonstrated by Sinclair et al. (1991) and Sinclair et al. (2007). Zanon et al. (2018) presented mean values of soybean development cycle duration in Rio Grande do Sul for different RMG and sowing dates. ...
The objective of this study was to determine the mean duration and the interannual variability of phenological subperiods and total soybean development cycle for 11 sowing dates in the humid subtropical climate conditions of the state of Rio Grande do Sul. Daily meteorological data were used from 1971 to 2017 obtained from the Pelotas agroclimatological station and from 1968 to 2017 from the main climatological station of Santa Maria. The soybean development simulation was performed considering three sets of cultivars of relative maturity groups between 5.9-6.8, 6.9-7.3 and 7.4-8.0, with intervals between the sowing dates of approximately 10 days, comprising September, 21 to December, 31. The data of phenological subperiods duration and total development cycle were subjected to the exploratory analysis BoxPlot, analysis of variance and mean comparison by the Scott-Knott test, with 5% of probability. The development cycle duration is greater in Pelotas than in Santa Maria. There was a decrease in soybean cycle duration from the first to the last sowing date for both locations. The R1-R5 subperiod duration is decreasing from October to December due to photoperiod reduction.
... Both in wheat and in soybean, less-stimulating photoperiods, that is short in wheat and long in soybean, delay both floral initiation and flowering and increase the number of vegetative primordia generated in the apex (Borthwick and Parker, 1938;rawson, 1971Wall and Cartwright, 1974;Halloran, 1977;Thomas and raper, 1977;Major, 1980;Hadley et al., 1984;Pinthus and nerson, 1984;raper and Kramer, 1987;roberts and Summerfield, 1987;Caffaro and nakayama, 1988;Sinclair et al., 1991;Evans and Blundell, 1994;Slafer and rawson, 1994aSlafer and rawson, ,b, 1995dSlafer and rawson, , 1996Upadhyay et al., 1994a,b;Fleming et al., 1997;Kantolic andSlafer, 2001, 2005;Kantolic et al., 2013;Zhang et al., 2001;Miralles et al., 2001Miralles et al., , 2003González et al., 2002). in line with the extended periods, non-stimulating photoperiods modify the number of tillers and the number of leaves per tiller in wheat and the number of branches and the number of nodes in the branches of soybean (Thomas and raper, 1983;Board and Settimi, 1986;Settimi and Board, 1988;Caffaro and nakayama, 1988;Kantolic and Slafer, 2001, 2005, 2007Kantolic et al., 2013). ...
... This trait was first identified in a plant introduction designated as P1 159925, and has been subsequently referred to as 'long-juvenile' (Parvez and Gardner, 1987;Hinson, 1989;Wilkerson et al., 1989), although there is no evidence that the trait alters the length of the juvenile period. The long-juvenile trait retards the overall development towards flowering, so that under short days the emergence-to-flowering period is longer in long-juvenile compared with normal genotypes (Sinclair et al., 1991(Sinclair et al., , 2005Sinclair and Hinson, 1992;Cairo and Morandi, 2006). This trait is useful in tropical and subtropical areas, as flowering can be delayed in spite of the prevailing conditions of high temperature and short photoperiod. ...
... Experiments with soybean isolines and crop modelling studies indicate that cultivars show variable sensitivities to photoperiod but a more similar response to temperature (Grimm et al., 1994;Upadhyay et al., 1994). Other simulation studies indicate that later MG cultivars require higher temperatures than the early MG cultivars to achieve similar rates of development towards flowering (Sinclair et al., 1991). Separate temperature functions for vegetative, flowering, and beginning seedfill allowed some authors to obtain better model predictions of developmental stages (Grimm et al., 1994;Piper et al., 1996;Setiyono et al., 2007), leading them to conclude that rate of development is less sensitive to temperature in later growth stages. ...
... Simulation studies and coefficient optimization tools might not be sufficient to develop robust photoperiod and temperature functions, even under a wide range of environments. For instance, several modelling approaches were able to predict similarly the date of flowering in soybean (Grimm et al., 1993;Sinclair et al., 1991). More studies under controlled conditions looking at the effect of temperature and photoperiod in development to flowering and later reproductive stages are needed to further improve the processes describing phenology in crop models and allow them to include measurable differences (or coefficients) at the cultivar level. ...
The use of crop models can be limited by the need to calibrate cultivar coefficients across a sufficiently wide range of environments. The DSSAT-CROPGRO-Soybean crop simulation model considers different temperature and photoperiod sensitivities during different crop developmental stages and/or for different cultivars. The use of generic phenology coefficients specific for a range of maturity groups (MGs) could allow accurate predictions of main developmental stages in soybean without requiring calibration. Phenology data collected in 2012 and 2013 from an irrigated regional planting-date experiment with maturity group (MG) 3 to 6 cultivars and latitudes from 30.6 to 38.9°N, were used to calibrate cultivar coefficients across all the environments. A set of generic coefficients were generated based on relative maturity group (rMG) and plant growth habit. Predictions of main developmental stages in the subsequent growing season (2014) using generic coefficients were similar to predictions based on calibrated coefficients, with a RMSE across all cultivars < 8 days. Several calibrations of cultivar coefficients were conducted testing different hypotheses of sensitivity to temperature and photoperiod in the model. Surprisingly, after the calibration, the model predicted with similar RMSEs the day of R1, first R5 seed, and R7 under the different hypothesis of model sensitivity to photoperiod and temperature. Therefore, the use of an optimization tool for calibration across several site x year x planting dates was efficient to obtain cultivar coefficients that minimized error in prediction, but did not provide meaningful insight regarding the mechanistic function of temperature and photoperiod coefficients describing phenology prediction.
... Regression slopes provided estimates of leaf appearance rates as affected by temperature. Rates were then regressed against the mean treatment temperature (i.e., 35/25 C at a 16/8 h day/night photoperiod results in a 24h mean temperature of 31.7 C) using a logistic equation similar to that used by Sinclair et al. (1991): ...
... Linear algorithms have frequently been used to describe plant development responses to temperature (Shaykewich 1995). However, recent work has shown that when high and low temperatures were included in the analyses, a nonlinear algorithm was most appropriate (e.g., Shaykewich 1995;Sinclair et al. 1991). A nonlinear, logistic function was used to describe the rate of leaf appearance as influenced by temperature (Figure 2). ...
Implementation of an integrated weed management system requires prediction of the effect of weed competition on crop yield. Predicting outcomes of weed competition is complicated by genetic and environmental variation across years, locations, and management. Mechanistic models have the potential to account for this variability. Weed phenological development is an essential component of such models. Growth cabinet: studies were conducted to characterize common ragweed's phenological response to temperature, photoperiod, and irradiance. Ragweed development occurred over a temperature range of 8.0 to 31.7 C, and this response to temperature was best characterized using a nonlinear funct ion. A maximum leaf appearance rate of 1.02 leaves d(-1) occurred at 31.7 C. Ragweed has a short juvenile phase, during which it was not sensitive to photoperiod. Following this juvenile phase, sensitivity to photoperiod was constant and continued until pistillate flowers were observed. Photoperiods of 14 h or less were optimal and resulted in maximum rates of development. Irradiance level affected ragweed phenological development only when combined with the additional stress of low temperatures. Data generated in this study can be used for the development of mechanistic weed competition models.
... Both in wheat and in soybean, less-stimulating photoperiods, that is short in wheat and long in soybean, delay both fl oral initiation and fl owering and increase the number of vegetative primordia generated in the apex ( Borthwick and Parker, 1938 ;Rawson, 1971Rawson, , 1993Wall and Cartwright, 1974 ;Halloran, 1977 ;Thomas and Raper, 1977 ;Major, 1980 ;Hadley et al., 1984 ;Pinthus and Nerson, 1984 ;Raper and Kramer, 1987 ;Roberts and Summerfi eld, 1987 ;Caffaro and Nakayama, 1988 ;Sinclair et al., 1991 ;Evans and Blundell, 1994;Slafer and Rawson, 1994ab, 1995dUpadhyay et al., 1994ab ;Fleming et al., 1997 ;Slafer, 2001, 2005 ;Zhang et al., 2001 ;González et al., 2002 ;Miralles et al., 2001Miralles et al., , 2003. In line with the extended periods, non-stimulating photoperiods modify the number of tillers the number of leaves per tiller in wheat and the number of branches and the number of nodes in the branches in soybean ( Thomas and Raper, 1983 ;Board and Settimi, 1986 ;Settimi and Board, 1988 ;Caffaro and Nakayama, 1988 ;Kantolic and Slafer, 2001, 2005, 2007. ...
... This trait was fi rst identifi ed in a plant introduction designated as P1 159925, and has been subsequently referred to as 'long-juvenile ' ( Parvez and Gardner, 1987 ;Hinson, 1989 ;Wilkerson et al., 1989 ), although there is no evidence that the trait alters the length of the juvenile period. The long-juvenile trait retards the overall development of the plants towards fl owering, so that under short days the duration of the emergence to fl owering period is longer in long-juvenile compared with normal genotypes ( Sinclair et al., 1991( Sinclair et al., , 2005Sinclair and Hinson, 1992 ;Cairo and Morandi, 2006 ). This trait is useful in tropical and sub-tropical areas, as fl owering can be delayed in spite of the prevailing conditions of high temperature and short photoperiod. ...
... Both in wheat and in soybean, less-stimulating photoperiods, that is short in wheat and long in soybean, delay both floral initiation and flowering and increase the number of vegetative primordia generated in the apex (Borthwick and Parker, 1938;rawson, 1971Wall and Cartwright, 1974;Halloran, 1977;Thomas and raper, 1977;Major, 1980;Hadley et al., 1984;Pinthus and nerson, 1984;raper and Kramer, 1987;roberts and Summerfield, 1987;Caffaro and nakayama, 1988;Sinclair et al., 1991;Evans and Blundell, 1994;Slafer and rawson, 1994aSlafer and rawson, ,b, 1995dSlafer and rawson, , 1996Upadhyay et al., 1994a,b;Fleming et al., 1997;Kantolic andSlafer, 2001, 2005;Kantolic et al., 2013;Zhang et al., 2001;Miralles et al., 2001Miralles et al., , 2003González et al., 2002). in line with the extended periods, non-stimulating photoperiods modify the number of tillers and the number of leaves per tiller in wheat and the number of branches and the number of nodes in the branches of soybean (Thomas and raper, 1983;Board and Settimi, 1986;Settimi and Board, 1988;Caffaro and nakayama, 1988;Kantolic and Slafer, 2001, 2005, 2007Kantolic et al., 2013). ...
... This trait was first identified in a plant introduction designated as P1 159925, and has been subsequently referred to as 'long-juvenile' (Parvez and Gardner, 1987;Hinson, 1989;Wilkerson et al., 1989), although there is no evidence that the trait alters the length of the juvenile period. The long-juvenile trait retards the overall development towards flowering, so that under short days the emergence-to-flowering period is longer in long-juvenile compared with normal genotypes (Sinclair et al., 1991(Sinclair et al., , 2005Sinclair and Hinson, 1992;Cairo and Morandi, 2006). This trait is useful in tropical and subtropical areas, as flowering can be delayed in spite of the prevailing conditions of high temperature and short photoperiod. ...
The understanding of crop phenology is critical for both improvement of yield potential and adaptation to stress. Matching ‘critical’ phases (when the most important yield components are determined) and best environmental conditions is crucial to maximize yield. The processes regulating crop development are complex and are strongly influenced by genetic and environmental factors. In this chapter, we describe the main developmental stages, delimiting major phenological phases of wheat and soybean, and the relationships between crop phenology, adaptation and yield determination. Analysis of environmental control of development is restricted to the main drivers: temperature, including temperature per se and vernalization (for wheat), and photoperiod. The effects of major genes are outlined: Vrn, Ppd and Eps in wheat and Dt (growth habit) and genes of the series E and J in soybean. Finally, we integrate the environmental and genetic effects on phenology into the determination of crop adaptation and yield potential.
... Both in wheat and in soybean, less-stimulating photoperiods, that is short in wheat and long in soybean, delay both fl oral initiation and fl owering and increase the number of vegetative primordia generated in the apex ( Borthwick and Parker, 1938 ;Rawson, 1971Rawson, , 1993Wall and Cartwright, 1974 ;Halloran, 1977 ;Thomas and Raper, 1977 ;Major, 1980 ;Hadley et al., 1984 ;Pinthus and Nerson, 1984 ;Raper and Kramer, 1987 ;Roberts and Summerfi eld, 1987 ;Caffaro and Nakayama, 1988 ;Sinclair et al., 1991 ;Evans and Blundell, 1994;Slafer and Rawson, 1994ab, 1995dUpadhyay et al., 1994ab ;Fleming et al., 1997 ;Slafer, 2001, 2005 ;Zhang et al., 2001 ;González et al., 2002 ;Miralles et al., 2001Miralles et al., , 2003. In line with the extended periods, non-stimulating photoperiods modify the number of tillers the number of leaves per tiller in wheat and the number of branches and the number of nodes in the branches in soybean ( Thomas and Raper, 1983 ;Board and Settimi, 1986 ;Settimi and Board, 1988 ;Caffaro and Nakayama, 1988 ;Kantolic and Slafer, 2001, 2005, 2007. ...
... This trait was fi rst identifi ed in a plant introduction designated as P1 159925, and has been subsequently referred to as 'long-juvenile ' ( Parvez and Gardner, 1987 ;Hinson, 1989 ;Wilkerson et al., 1989 ), although there is no evidence that the trait alters the length of the juvenile period. The long-juvenile trait retards the overall development of the plants towards fl owering, so that under short days the duration of the emergence to fl owering period is longer in long-juvenile compared with normal genotypes ( Sinclair et al., 1991( Sinclair et al., , 2005Sinclair and Hinson, 1992 ;Cairo and Morandi, 2006 ). This trait is useful in tropical and sub-tropical areas, as fl owering can be delayed in spite of the prevailing conditions of high temperature and short photoperiod. ...
Publisher Summary
This chapter discusses the particularities of development of wheat and soybean to highlight the importance of identifying the genetic and environmental controls of the phenological pattern. This knowledge is a prerequisite to understand, predict, and manipulate the association between crop cycles, the resources, and the environmental constraints to favor the coincidence of the critical period with the most favorable conditions. Although the cycle to match crops and environmental factors has been determined in most production systems, further improvement is feasible by manipulation of critical periods. The critical period may occur before (e.g., in wheat) or after (e.g., in soybean) flowering, but it is clear that in both species, in spite of their large morphological and physiological differences, the growth during this period defines crop yield in most environments. Improving the knowledge of genetic and environmental drivers of the expression of the genes that control flowering time should improve the precision in positioning the critical period, when the highest level of resources is expected and stresses are less likely.
... Furthermore, one of the key indicators of the variation of photoperiod response among cultivars and an important criterion for measuring the rate of development in soybean is the critical photoperiod (Yang et al., 2019). The critical photoperiod is the point at which soybean flowering is inhibited, marking the boundary between allowing and preventing flowering based on photoperiod (Steinberg and Garner, 1936;Sinclair et al., 1991). ...
Soybean is a highly photoperiod-sensitive and typically quantitative short-day plant. Photoperiodism is a rhythmical shift in sensitivity to light to help plants control flowering time following seasonal changes in day length and to adapt to growing conditions at different latitudes. Photoperiod is one of the dominant abiotic factors affecting all of the phenological stages and the adaption of soybean to a specific environment. Although soybean is grown in a wider geographical region of 53°N to 40°S latitudes, the cultivation of each variety of soybean is restricted to a narrow latitudinal range of ~200 km due to their photoperiod sensitivity, and soybean genotypes show a great diversity in response to photoperiod. The response of soybean to photoperiod affects not only the beginning of flowering but also the post-flowering vegetative and reproductive processes like seed setting, pod filling duration, terminal inflorescence development, yield, seed quality, leaf senescence and maturity. Flowering time, maturity, and other quantitative traits associated with photoperiod adaptability are governed by multiple genes with major-effect and minor-effect, environmental factors and genotype-by-environment interactions. Allelic variations at loci governing photoperiod sensitivity and growth result in maturation differences and have pleiotropic effects on seed yield and other agronomic traits, comprising plant height, lodging, seed weight and composition, and tolerance to various environmental stresses and diseases. Besides the photoperiodic response of soybean, maturity group and critical photoperiod can be indicators of the photosensitivity of the plant. The critical photoperiod is referred to as the photoperiod at which soybean flowering is inhibited, and it is a border between allowing flowering and inhibiting flowering and is an important indicator reflecting the variation of photoperiod sensitivity among varieties. The maturity group system is used to classify soybean varieties according to their photothermal (photoperiod + temperature) response and is very sensitive to photoperiod. Soybean varieties were classified into 13 maturity groups from 000 to X, providing a robust approach to their environmental adaptation. Consequently, understanding and manipulating genetic variation in the photoperiodic control of soybean flowering time is crucial for adapting to new ecological zones.
... More than 90% of edible oil consumed is imported into Iran. Soybean, which is one of the primary sources of high-quality oil and protein, is commonly used in human food consumption (Sinclair et al., 1991). Maize is one of the most widely used agricultural products in Iran. ...
Aim of study: To evaluate the performance of DSSAT and AquaCrop models in the estimation of soybean and grain maize yield under water stress conditions in a semi-arid region.
Area of study: Kermanshah, Iran.
Material and methods: AquaCrop and DSSAT were assessed to simulate soybean and maize. Both models were calibrated using field data. Field experiments were performed in a randomized complete block design with eight and four irrigation treatments for soybeans and maize, respectively with three replications. Measures of Normalized Root Mean Square Error (nRMSE) and Nash-Sutcliffe Model Efficiency were used to evaluate the accuracy of the models. For this purpose, simulated values of leaf area index / green crop canopy, grain yield, biomass, and soil moisture were compared with measured data.
Main results: Results indicated that the CROPGRO-Soybean in DSSAT software simulated more accurate crop growth of soybean than AquaCrop. The average nRMSE of the DSSAT model for estimating soil moisture, leaf area index, grain yield, and biomass were 6%, 14%, 16% and 20%, respectively. For maize, AquaCrop simulated crop growth more reliably than CERES-maize. The average nRMSE of 3%, 10%, 13% and 27% of the Aquacrop model in simulating the parameters of soil moisture, green crop canopy, biomass, and grain yield.
Research highlights: Considering the better performance of AquaCrop for maize and DSSAT for soybean in the study area, it is not possible to propose a specific model to simulate the growth of all crops in a region.
... Photoperiod before flowering was also a significant covariate (r 2 0.106 at the 4th to 5th stages, −log10P 33.286). These results are reasonable considering that temperature and photoperiod are the main determinants of soybean flowering time (Sinclair et al., 1991). The results obtained for DTM and SL were close to those obtained for DTF, suggesting that the influence of meteorological factors on the G × E interactions for DTM and SL occurred via those of DTF. ...
It has not been fully understood in real fields what environment stimuli cause the genotype-by-environment (G × E) interactions, when they occur, and what genes react to them. Large-scale multi-environment data sets are attractive data sources for these purposes because they potentially experienced various environmental conditions. Here we developed a data-driven approach termed Environmental Covariate Search Affecting Genetic Correlations (ECGC) to identify environmental stimuli and genes responsible for the G × E interactions from large-scale multi-environment data sets. ECGC was applied to a soybean (Glycine max) data set that consisted of 25,158 records collected at 52 environments. ECGC illustrated what meteorological factors shaped the G × E interactions in six traits including yield, flowering time, and protein content and when these factors were involved in the interactions. For example, it illustrated the relevance of precipitation around sowing dates and hours of sunshine just before maturity to the interactions observed for yield. Moreover, genome-wide association mapping on the sensitivities to the identified stimuli discovered candidate and known genes responsible for the G × E interactions. Our results demonstrate the capability of data-driven approaches to bring novel insights on the G × E interactions observed in fields.
... These results are reasonable considering that temperature and photoperiod are the main determinants 510 of soybean flowering time (Sinclair et al., 1991). The results obtained for DTM and SL were close to 511 those obtained for DTF, suggesting that the influence of meteorological factors on the G × E 512 interactions for DTM and SL occurred via those of DTF. ...
It has not been fully understood in real fields what environment stimuli cause the genotype-by-environment (G × E) interactions, when they occur, and what genes react to them. Large-scale multi-environment data sets are attractive data sources for these purposes because they potentially experienced various environmental conditions. Here we developed a data-driven approach termed E nvironmental C ovariate Search Affecting G enetic C orrelations (ECGC) to identify environmental stimuli and genes responsible for the G × E interactions from large-scale multi-environment data sets. ECGC was applied to a soybean ( Glycine max ) data set that consisted of 25,158 records collected at 52 environments. ECGC illustrated what meteorological factors shaped the G × E interactions in six traits including yield, flowering time, and protein content and when they were involved. For example, it illustrated the relevance of precipitation around sowing dates and hours of sunshine just before maturity to the interactions observed for yield. Moreover, genome-wide association mapping on the sensitivities to the identified stimuli discovered candidate and known genes responsible for the G × E interactions. Our results demonstrate the capability of data-driven approaches to bring novel insights on the G × E interactions observed in fields.
Key message
The proposed method is able to identify environmental stimuli and genes responsible for the G × E interactions observed in multi-environmental trials. The method is based on similarity search between genetic correlation and environmental stimuli among environments.
... Applying speed breeding to short-day species such as soybean is challenging since these plants' reproductive cycle is controlled by critical day length (CDL) (Thomas & Vince-Prue, 1997), a threshold duration of light exposure above which flower production is restricted, and their reproductive development rate is maximized under an optimum day length. In soybean, an understanding of CDL, optimum day length, and temperature is essential to predict the time required to reach each phenological stage (Cregan & Hartwig, 1984;Sinclair, Kitani, Hinson, Bruniard, & Horie, 1991). Sensitivity to photoperiod and its associated effects have long been documented in soybean (Garner & Allard, 1930), with a negative linear relationship between CDL and maturity group (MG). ...
Soybean [Glycine max (L.) Merr.] breeding involves crossing and inbreeding for multiple generations to develop genetically stable lines. The long generation times cause early generations to be the major bottleneck in soybean breeding. Here we tested the effect of red and blue light (RB) and full‐spectrum white light (FS), coupled with 12‐h light (29 °C) vs. 12‐h darkness (27 °C) photothermal conditions, on the growth and development of soybean lines and breeding materials of diverse maturity groups (MGs) in a context of speed breeding. We observed that RB light vs. FS light reduced plant height but did not affect vegetative biomass, pods and seeds per plants, nor the ability to meet a minimum of one seed per plant. Overall, the RB treatment reduced the interval planting to physiological maturity by 1.5 d vs. the FS treatment. The period between planting and harvest of mid‐ and late‐maturity soybean ranged from 63 to 81 d, vs. ∼120 d observed in field conditions. Also, days after planting (DAP) to R7 was dependent on soybean MG. The use of RB light, coupled with photothermal conditions herein reported, would allow to advance up to five generations of U.S.‐adapted soybean under a controlled environment instead of the one to three generations currently possible. This methodology is simple and easily scalable, for it maintains stable growing conditions throughout the crop cycle and it allows for simultaneous planting and harvesting within the same growth room. This could have a significant impact in genetic gain of U.S. soybean breeding programs.
... The above studies have exploited plant morphological and developmental characteristics, including plant height, mainstem nodes, shoot biomass, stand counts, and soybean yields for accessing differential responses to cultural practices resulting from maturity and growth habit. Recent studies have also recognized the effects of low and high temperatures at different stages of soybeanbased on plant morphological and physiological characteristics (Alsajri et al., 2020;Edwards, 1934;Lin et al., 1984;Sinclair et al., 1991;Tacarindua et al., 2013). However, the literature on responses of early maturing soybean cultivars differing in growth habit to varying temperatures is limited. ...
Temperature affects plant growth at all stages, and it is important to quantify soybean early-season responses to a wide range
of temperatures. This research was conducted to determine whether seedlings of two soybean cultivars with different
growth habits respond differently to increasing temperature. The effect of five day/night temperature regimes of 20/12,
25/17, 30/22, 35/27, and 40/32 °C on the below- and aboveground growth and development of Asgrow AG5332 (AG) and
Progeny P5333 RY (PR) soybean cultivars with an indeterminate and determinate growth habit, respectively, were evaluated
under controlled conditions from seedling emergence to 21 days after sowing. Temperature and cultivar interacted to affect
plant height, root surface area, and root tips, while the main cultivar effect influenced the number of mainstem nodes and
root volume. For all other root and shoot parameters, the main temperature effect was significant. Our analysis indicated that
from emergence until 21 days after sowing, the evaluated root and shoot parameters for the cultivars responded similarly to
increasing day/night temperature regime, and plant responses to temperature were best described by quadratic functions. The
functional algorithms developed during this research should be incorporated into crop simulation models to help predict
the effect of increasing temperature on the growth and development of soybean across the US.
... Occurrence date of the first trifoliate leaf emission stage (V2) was estimated using the Soydev model (SETIYONO et al., 2007). The beginning of flowering (R1) and beginning of seed (R5) date were simulated with the models proposed by Sinclair et al. (1991) and Sinclair et al. (2007), respectively. The date of physiological maturity (R7) stage was obtained by calculating the thermal time and the date of harvest maturity (R8) was simulated by the model proposed by Sinclair (1986). ...
ADJUSTMENT OF PROBABILITY FUNCTIONS TO WATER EXCESS AND DEFICIT IN SOYBEANS CULTIVATED IN LOWLAND SOILS MATEUS POSSEBON BORTOLUZZI¹; ARNO BERNARDO HELDWEIN²; ROBERTO TRENTIN³; ASTOR HENRIQUE NIED²; JOCÉLIA ROSA DA SILVA4 E LEIDIANA DA ROCHA4 1 Faculdade de Agronomia e Medicina Veterinária, Universidade de Passo Fundo, BR 285, São José, 99052-900, Passo Fundo, RS, Brasil. mateusbortoluzzi@upf.br 2 Departamento de Fitotecnia, Universidade Federal de Santa Maria, Avenida Roraima, n° 1000, Camobi, 97010-900, Santa Maria, RS, Brasil. heldweinab@smail.ufsm.br; astor.nied@ufsm.br 3Departmento de Fitotecnia, UFPel, Campus Universitário, s/n, 96010-610, Capão do Leão, RS, Brasil. roberto.trentin@ufpel.edu.br 4 Programa de Pós-graduação em Agronomia, Universidade Federal de Santa Maria, Avenida Roraima, n° 1000, Camobi, 97010-900, Santa Maria, RS, Brasil. joceliarosa.s@gmail.com; leidi-r1@hotmail.com 1 ABSTRACT The objective of this study was to verify the fit of exponential, gamma, lognormal, normal and weibull probability density functions (pdf) to water deficit and excess accumulated data during soybean subperiods and development cycle. Historical series of meteorological data obtained from Pelotas and Santa Maria meteorological stations (RS) were utilized. The soybean development simulation was performed for cultivars from the relative maturity group (RMG) between 5.9-6.8, 6.9-7.3 and 7.4-8.0 on eleven sowing dates from September 21 to December 31. Daily sequential water balance was calculated with water excess (days) and water deficit (mm) data to adjust each pdf to the observed data. The better adjustment frequency for water excess data in the soybean cycle was obtained with normal pdf in Santa Maria and weibull and gamma in Pelotas. Regardless of the location, the lognormal pdf presented the best fit for the water deficit data in the soybean cycle. In both locations, normal and weibull pdf demonstrated the best performance for water excess in the subperiods gamma, lognormal and exponential pdf for the water deficit. Keywords: Glycine max, risk analysis, sowing date, historical series. BORTOLUZZI, M. P.; HELDWEIN, A. B.; TRENTIN, R.; NIED, A. H.; DA SILVA, J. R.; DA ROCHA, L. AJUSTE DE FUNÇÕES DE PROBABILIDADE AO EXCESSO E DÉFICIT HÍDRICO NA SOJA EM TERRAS BAIXAS 2 RESUMO O objetivo deste trabalho foi verificar o ajuste das funções densidade de probabilidade (fdp) exponencial, gama, lognormal, normal e weibull aos dados de déficit e excesso hídrico, acumulados durante subperíodos e ciclo de desenvolvimento da soja. Foram utilizadas séries históricas de dados meteorológicos obtidos das estações meteorológicas de Pelotas e de Santa Maria, RS. Foi simulado o desenvolvimento da soja, para cultivares de grupo de maturidade relativa (GMR) entre 5.9–6.8, 6.9–7.3 e 7.4–8.0 em onze datas de semeadura compreendidas entre 21 de setembro e 31 de dezembro. Calculou-se o balanço hídrico sequencial diário, sendo obtidos os dados de excesso hídrico (dias) e déficit hídrico (mm) para ajustar cada fdp aos dados observados. A maior frequência de ajuste para os dados de excesso hídrico no ciclo da soja foi obtida para a fdp normal em Santa Maria e fdp weibull e gama para Pelotas. A fdp lognormal foi a que melhor se ajustou aos dados de déficit hídrico no ciclo da soja, independentemente do local. Em ambos os locais, a fdp normal e a weibull apresentaram o melhor desempenho para o excesso hídrico nos subperíodos e as fdps gama, lognormal e exponencial para o déficit hídrico. Palavras-chave: Glycine max, análise de risco, data de semeadura, séries históricas.
... Different from the reproductive growth period (RGP), development rate during the vegetative growth period (VGP) is additionally affected by photoperiod (Sinclair, Kitani, Hinson, Bruniard, & Horie, 1991). Depending on the effect of day length on development rate, crops can be divided into long-day species (e.g., winter wheat [Triticum aestivum L.]) and short-day species (e.g., single rice [Oryza sativa L.], maize [Zea mays L.], and spring soybean [Glycine max (L.) Merr.]) (Blanchard & Runkle, 2010;Yano, Kojima, Takahashi, Lin, & Sasaki, 2001). ...
Although crop phenology is responsive and adaptable to cultural and climatic conditions, many phenology models are too sensitive to variable climatic conditions. We developed a plastic temperature response function by assuming that development rate was linearly related to temperature and that the linearity was linearly responsive to day of year (DOYv) of the starting date of the vegetative growth period (VGP). Phenology observations and weather data were acquired for winter wheat (Triticum aestivum L.), rice (Oryza sativa L.), maize (Zea mays L.), and soybean [Glycine max (L.) Merr.] at 12 locations over 15–26 yr. Additional data were observed for maize grown in an interval planting experiment. For 78.6% of the sites, the crop development rate during the VGP was positively affected by DOYv. Partial correlation analysis (controlling for temperature) indicated that DOYv was independent of temperature. When averaged over all crops and sites, the RMSE for a plastic phenology model based on both response and adaptation mechanisms was lower (RMSE = 2.81 d) than models (RMSE = 3.39) based only on response mechanism (p < .01). Furthermore, simulations produced by the plastic model showed less bias to DOYv, temperature, and year. The plastic function provided a simple and effective method for achieving better phenology simulation accuracy. According to the plastic function, growing season under warming conditions will not be reduced by as much as simulated by models based only on response mechanism, so yield loss due to warming is likely to be overestimated.
... There are two definitions of critical photoperiod. One is that critical photoperiod is the photoperiod above which soybean flowering is prevented, and it is the demarcation of photoperiod between allowing flowering and preventing flowering (Steinberg and Garner, 1936;Sinclair et al., 1991). Another definition is that critical photoperiod is the photoperiod above which soybean flowering is delayed, and it is the dividing point between photoperiod sensitivity and photoperiod insensitivity 15.8, 15.3, 14.7, 13.4 to 13.7, and £12 h d −1 , respectively. ...
Soybean [Glycine max (L.) Merr.] is a photoperiod‐sensitive crop, and the photoperiod response determines the ecological adaptability of soybean genotypes. Critical photoperiod is the dividing daylength between photoperiod sensitivity and photoperiod insensitivity phases and is one of the most important indicators of photoperiod sensitivity. However, the appropriate experimental treatment and calculation method for quantifying the critical photoperiod are poorly documented. To characterize the photoperiod response of genotypes, 72 soybean genotypes belonging to 14 different maturity groups (MG 0000–MG X) were included, and five photoperiod treatments of 12‐, 14‐, 16‐, 18‐, and 20‐h daylength were conducted in the consecutive 3 yr from 2015 to 2017. The piecewise linear regression model based on the median function was used to determine the critical photoperiod. The results showed that the photoperiodic responses of soybean genotypes were significantly different among various MGs. The critical photoperiod of MG 0000 was 16.4 h d⁻¹, whereas those of MG 000 to MG I, MG II to MG III, MG IV, MG V to MG VIII, and MG IX to MG X were 15.7 to 15.8, 15.3, 14.7, 13.4 to 13.7, and ≤12 h d⁻¹, respectively. A significant negative linear relationship between the critical photoperiod and relative maturity group (RMG) was found. It is of particular importance for the quantification of soybean photoperiod response and precise prediction of the developmental process. More importantly, the critical photoperiod obtained in this study will help breeders to synchronize the flowering time of parents from distant geographic origins and break the reproductive isolation among different ecotype cultivars.
... Different studies (Hodges and Frech, 1985;Nissly et al., 1981) have emphasized the interaction between temperature and day length on soybean development stages and have shown that with increasing temperature and decreasing the length of day, plant development rate increasing. Sinclair et al. (1991), in their studies on 9 soybean cultivars, calculated a linear model(below model) with a coefficient of explanation between 74 and 89 percent. ...
... Diversos modelos se han propuesto para la predicción del tiempo a floración de cultivos en ambientes fluctuantes, los que se basan en valores promedios de fotoperíodo (P) y temperatura (T) entre siembra y floración (Angus et al., 1981;Perry et al., 1987;Sinclair et al., 1991;Yan y Wallace, 1998). En general estos modelos asumen que la sensibilidad a fotoperíodo es constante y uniforme durante todo el tiempo previo a la floración. ...
... during which plant growth and development is insensitive to photoperiod; (ii) an inductive phase, during which growth rates are influenced by photoperiod; and (iii) a postinductive phase, during which the growth rate is unaffected. Quantification of the influence of photoperiod and temperature on development has been conducted with different types of models (Hodges, 1991;Sinclair et al., 1991). However, there remains some confusion about whether the flowering and pod growth rates should depend only on photoperiod experienced during the flower and pod inductive phase or during both the pre-and inductive phases, although it is widely accepted that the preflower inductive stage of most crops is not influenced by photoperiod (Wilkerson et al., 1989), Saxena (1984) concluded that there were no indications of pronounced juvenile phase in chickpea (Cicer arietinum L.). ...
Photoperiod and temperature are important environmental factors that affect the adaptation of bambara groundnut (Vigna subterranea Verdc.) and other crops to hostile climates in the tropics. The use of the accumulation concept, in which the relative rate of progress towards podding sums up to one is a common methodology. However, the lack of quantitative information, has resulted in poor decision making for crop management practices in relation to the time of optimum pod growth rate (Ropt). This study investigated modeling pod growth using an additive and interactive relation between pod growth rate, mean photoperiod, and temperature during the pod inductive phase. The field experiment was conducted at Ekpoma Nigeria. Ten bambara groundnut landraces from three different regions in Nigeria (Anyigba, Otukpo, and Nsukka) were sown on six dates from 15 June to 1 September during the successive growing seasons of 2010 to 2012, thus exposing the landraces to mean natural photoperiods of 12 h 23 min, 12 h 19 min, 12 h 14 min, 12 h 10 min, 12 h 5 min, 12 h, 11 h 55 min, 11 h 51 min, and 11 h 47 min during the pod inductive period. The observed optimum photoperiod and temperature for Ropt were 12 h and 26°C, respectively, for all landraces. Allocation to pod growth began at the critical photoperiod (Pc) of 12 h 19 min for Otukpo landraces, whereas Pc for Nsukka landraces was 12 h 14 min. However, the Pc for Anyigba landraces occurred earlier at 12 h 23 min. The pod growth model that was developed provided good predictions of pod growth for a natural range of photoperiods and temperatures.
... For soybean cultivars of similar MG ratings, a specific average number of heat units (measured by GDD) are required from planting to maturity. A number of studies investigated the effect of temperature and photoperiod on soybean growth and development (Brown, 1960;Major et al., 1975b;Sinclair et al., 1991;Elizondo et al., 1994;Câmara et al., 1997). Light intensity also affects growth and development (Ramesh and Gopalaswamy, 1991). ...
Farmers in North Dakota and Northern Minnesota did not have a model to predict when their soybean (Glycine max L. Merr.) crop will be mature. Soybean plants need to be mature before the first fall freeze. The objectives of this study were to estimate needed accumulated growing degree days (AGDD) for adapted soybean maturity groups (MG) to reach maturity (R8). Research was conducted during 2007–2012 at northern, central, and southern North Dakota, to develop a model to predict the soybean maturity date based on accumulated GDDs and to verify the model using field research data from 2013 to 2015. Based on 1816 data points a regression analysis was performed which predicted that 1666, 1862, 2030 AGDD (with a 50 °F base temperature) were needed to reach maturity for 00.7, 0.4, and 1.0 MG soybean cultivars, respectively.
... The objective of this study is threefold: (a) to investigate safflower seed-germination rates under variable temperature and water potential conditions; (b) to estimate cardinal temperature parameters for safflower seed germination using two well-known modeling approaches-the hydrothermal time and the multiplicative models (Sinclair et al. 1991;Grimm et al. 1993;Rowse and Finch-Savage 2003;Hardegree 2006a, b;Hardegree and Winstral 2006;Soltani et al. 2006a, b;Soltani et al. 2013); and (c) to discuss differences in parameter estimations between the two models and evaluate their predictive abilities against independent dataset. The multiplicative modeling approach has been used to model temperature and photoperiod effects on plant development (e.g., Setiyono et al. 2010), and temperature and vernalization effects on plant development (e.g., Ritchie 1991;Jones et al. 2003), but to our knowledge, there is no application that combines temperature and moisture effects on seed germination. ...
Seed germination is a biological process that is strongly affected by temperature and water potential. Our objective was to measure experimentally and model this combined effect and estimate robust parameter values that will assist researchers to estimate safflower germination rate under variable experimental conditions. A laboratory experiment was conducted to investigate the combined effect of seven temperatures regimes (10, 15, 20, 25, 30, 35 and 40 °C) and five water stress levels (0, −0.4, −0.8, −1.2 and −1.6 MPa) on safflower seed germination. The derived dataset was analyzed using two modeling approaches that combine temperature and water potential effects: the multiplicative and the hydrothermal time models. The associated parameter estimates for each model were determined through statistical optimization and model performance evaluated against an independent dataset. The hydrothermal time parameters were 493.3 MPa h, 8.2 °C, and −1.34 MPa for θ
HT (hydrothermal time constant) T
b (base temperature), and ψ
b(50) (median base water potential) in sub-optimal temperatures, respectively. The parameter estimates for the multiplicative model were determined as 7.9 °C for T
b, 21.4 °C for T
o1 (lower optimal temperature), 29 °C for T
o2 (upper optimal temperature), and 40 °C for T
c (ceiling temperature); 0 MPa for WPc (critical water potential) and 1.18 h−1MPa−1 for water potential sensitivity coefficient (WPS); and 17.9 h for g
o (physiological hours for seed germination). Model evaluation showed that the multiplicative model predicted time to 50 % of seed germination more accurately (RMSE = 4.3 h and R
2 = 0.98) than the hydrothermal time model (RMSE = 9.5 h and R
2 = 0.93).
... The role of photoperiod x temperature interaction in controlling flowering processes has been one of the key research problems in the understanding of the growth of some crops such as maize and soybean [Glycine max (L). Merr] (Jean, 2009;Cregan and Hartwig, 1984;Egli et al., 1989;Mcblain et al., 1987;Sinclair et al., 1991;Wang et al., 1987). Therefore the objectives of this study were to evaluate 100 maize varieties for flowering traits and determine the climatic factors influencing the interaction of the environments with days to flowering (GxE) in the rainforest ecology of South West Nigeria. ...
The number of days from planting to flowering in maize (Zea mays L.) is of interest to maize breeders because of its importance in the selection of appropriate hybrid parents. Highly significant interaction of planting dates with varieties for flowering traits have been observed during the early and late cropping seasons in the rainforest agro-ecology of South West Nigeria. This makes the use of flowering dates as indicators of maturity unreliable. Therefore the objectives of this study were to evaluate 100 maize varieties for flowering traits and determine the climatic factors influencing the interaction of the environments with days to flowering (GxE) in the rainforest ecology of South West Nigeria. One hundred maize varieties were evaluated during the late and early cropping seasons of 2007/2008 and 2008/2009. Significant differences were observed among the varieties for flowering traits (days to 50% tasseling, anthesis and silking). There was also significant variety x season interaction mean squares. In the early season, TZEE-WSRBC 5, TZEEPOPSTRCo and 97TZEE-Y-2C1 with 47-53 days to full flowering were the earliest to flower while Oba-Super II and ACR96DMR-LSR W with 64-71 days to flowering were the latest to flower. In the late seasons, 2004TZEE-WPOPSTRC4, TZEEPOPSTRCo, SINETEE-WSR and TZE-WPOPDTSTRC4F2 were the earliest to flower (42-47 days) while BUSOLA STR, TZLCOMPCO, 9021-18STR and Oba super II were the latest (61-68 days). Flowering interval was shorter in the late than the early season regardless of the maturity group with temperature as main climatic factor influencing flowering in this ecology.
... Para a condição potencial, eliminaram-se os algoritmos de resposta ao deficit hídrico do modelo de Sinclair (1986). As datas de término de crescimento foliar e início do enchimento de grão foram simuladas para a cultivar Bragg, com o modelo nãolinear de resposta do desenvolvimento à temperatura e ao fotoperíodo propostos por Sinclair et al. (1991), utilizando-se dados de um ensaio realizado no campo experimental do Departamento de Fitotecnia da UFSM, Santa Maria, RS, no ano 1983/1984, sendo que o término do crescimento foliar corresponde ao dia de estádio de desenvolvimento 1 e o início do enchimento de grãos ao dia de estádio 1,8 do modelo de Sinclair et al. (1991). ...
The objective of this study was to simulate soil water content, and wheat, soybean and maize yields, in Santa Maria, RS, Brazil, and link their interannual variability to El Niño Southern Oscillation (ENSO). The period studied was 1969 to 2003. Soil water content and the yields of wheat, soybean and maize were simulated with models available in the literature. Soil water content was represented by the fraction of transpirable soil water. The results showed that the lowest soil water content in Santa Maria is associated to neutral years and the highest soil water is associated to El Niño events. La Niña years were more favorable to high wheat yield, whereas El Niño years were more favorable to high soybean and maize yields. It was evident that years classified as neutral years in respect to ENSO are riskier to grain yields of soybean and maize crops, which is an important information for planning strategies in agribusiness considering ENSO forecast.
... No modelo sobre a soja (Sinclair, 1986), as datas de término de crescimento foliar e início do enchimento de grãos foram simuladas com o modelo não-linear de resposta do desenvolvimento, à temperatura e fotoperíodo propostos por Sinclair et al. (1991), utilizando-se dados da cultivar Bragg publicados em Schneider et al. (1984), para a estimativa dos coeficientes nas condições locais. No modelo sobre o milho, os coeficientes foram os mesmos de Muchow & Sinclair (1991). ...
The objective of this work was to evaluate through a numerical study the impact of a possible climate change on the yield of wheat, soybean and maize, in Santa Maria, RS. Climate change scenarios were created by doubling CO2, with different increases in air temperature, and with increases and without increases in rainfall. Yield of the three crops was simulated with models available in the literature. It was concluded that projected climate change will affect wheat, soybean and maize yield in Santa Maria, RS, Brazil. The increase of 2, 3 and 6 degrees C may cancel the benefits of increasing CO2 on yield of wheat, soybean and maize, respectively.
... Predicting flowering within 2 days of observed is, at worst, equivalent in accuracy to the models derived for other crops (e.g. Angus et al. 1981;Hammer et al. 1989;Loss et al. 1990;Jones et al. 1991;Sinclair et al. 1991). However, for cv. ...
Assessment of the potential for mungbean cropping in the Australian monsoon tropics required a model that could predict pre-harvest seed quality from long-term climatic data. Empirical relations between seed quality and pre-harvest weather were developed from field-grown mungbean using 22 sowings over 3 seasons. Seed quality reflected visual symptoms of weather damage expressed as the percentage of undamaged seed. A minimum exposure to rainfall was required before seed quality was reduced. After this minimum was exceeded, the effect of additional rainfall was cumulative and the percentage of unweathered seed decreased proportionally until a maximum was reached whereby all susceptible seed was weather damaged. The percentage of unweathered seed was best predicted as a function of the cumulative duration of rainfall events. Exposure to at least 300 min of rainfall was required before seed quality was downgraded. Exposure to 4000 min of rainfall was required to reach the maximum threshold. The linear decline in the percentage of unweathered seed was accurately predicted with independent data (r(2) = 0.84) by a function that combined the cumulative duration of rainfall and the standard deviation of evaporation. This function reflected the weathering process, that is, cumulative exposure to moisture and the extent of drying of the atmosphere between rainfall events. Alternatively, where pluviograph data were unavailable, combining the sum of rainfall events (>0.5mm) with the standard deviation of evaporation and mean daily solar radiation was also highly correlated with the proportion of unweathered seed; accurate predictions were made using independent data during crop ripening (r(2) = 0.93) and after ripening (r(2) = 0.72). Weather damage was sensitive to the timing of reproductive development relative to rainfall; adjusting climate variables for cohort-specific exposure removed the confounding effects caused by the daily ripening of pods. Time to flowering was accurately predicted, 2-3 days from observed, using mean daily photoperiod and temperature. As expected, rate of progress from flowering to the first ripe pod and crop maturity was dependent on photoperiod, temperature, and moisture availability. The proportion of pods ripe on any day was highly (P < 0.01) correlated with the proportion of the pod-ripening phase completed.
... Predicting flowering within 2 days of observed is, at worst, equivalent in accuracy to the models derived for other crops (e.g. Angus et al. 1981;Hammer et al. 1989;Loss et al. 1990;Jones et al. 1991;Sinclair et al. 1991). However, for cv. ...
To demonstrate a model to simulate the risk of weather damage of mungbean, application studies were undertaken using 27 years of climatic data collected at Katherine, Northern Territory, Australia. In terms of the risk of weather damage, the transition from high risk to low risk occurred after mid-February but before 20 March. High quality seed could be expected in 70% of seasons for a crop that matured after 20 March. For planting dates prior to 25 January, the chance of producing premium quality seed was enhanced to 40-70% of seasons by sowing a cultivar that matured 2 weeks later and by harvesting promptly (4 days after maturity). There was no benefit from later maturity or harvest promptness where sowing was made after 25 January, because maturity occurred after the wet season was complete. In contrast, yield was optimised at early January sowing dates. Calculating gross margins by combining yield and weather damage simulations identified an optimum sowing date between the optimum for yield and seed quality. It was shown that later maturity combined with photoperiod sensitivity increased the sowing window from 10 to 29 days compared with a short duration variety that was insensitive to photoperiod. The relative merits of modelling and field experimentation in assessing the cropping potential for mungbean in a new region are discussed. The need to be able to simulate the yield of the second flush of flowers was acknowledged as a future research requirement.
... No modelo sobre a soja (Sinclair, 1986), as datas de término de crescimento foliar e início do enchimento de grãos foram simuladas com o modelo não-linear de resposta do desenvolvimento, à temperatura e fotoperíodo propostos por Sinclair et al. (1991), utilizando-se dados da cultivar Bragg publicados em Schneider et al. (1984), para a estimativa dos coeficientes nas condições locais. No modelo sobre o milho, os coeficientes foram os mesmos de Muchow & Sinclair (1991). ...
O objetivo deste trabalho foi avaliar, por meio de estudo numérico, a existência de impacto da possível mudança climática sobre o rendimento das culturas do trigo, soja e milho, em Santa Maria, RS. Foram criados cenários de mudança climática, dobrando-se a quantidade de CO2, com diferentes aumentos de temperatura do ar, com aumento e sem aumento de precipitação pluvial. O rendimento das três culturas foi simulado com modelos matemáticos disponíveis na literatura. Concluiu-se que a mudança climática, projetada pela simulação, influenciará o rendimento de grãos de trigo, soja e milho, em Santa Maria, RS. O aumento de 2, 3 e 6ºC na temperatura do ar pode anular os efeitos benéficos do aumento de CO2 no rendimento de trigo, soja e milho, respectivamente.
... Major et al. [12] used a multiplicative model of temperature and photoperiod to describe the time of flowering of soybean. Sinclair et al. [15] used linear and logistic models based on temperature and photoperiod to predict the date of flowering of soybean crops. ...
Predicting the time of soybean flowering is a critical step for crop management
practices and for the development of crop models. The main objective of this
study was to quantify the effect of the photoperiod and of temperature on the
duration of the different phenological periods (flowering, first pod and
physiological maturity), and to evaluate the response of a simple linear model for
predicting phenological periods in Azul, centre of Buenos Aires, Argentina. It
also used the methodology defined in the work of Summerfield et al.
(Measurement and prediction of flowering in soybeans in fluctuating field
environments. In: World Soybean Research Conference 4, 1989, Buenos Aires.
Argentina Soybeans Association, 1989. pp. 82-87) which generates families of
mathematical models with non-linear parameters and includes the study of linear
models to obtain other models. Finally the sensitivity of the models to the
variations produced by the experimental data was studied by applying the
methodology used in Summerfield et al., Verdu and Villacampa (A computer
program for a Monte Carlo analysis of sensitivity in equations of environmental
modelling obtained from experimental data. Advances in Engineering Software.
Vol. 33, Nº 6. pp.351-359, 2002) and Verdu and Villacampa (A Computational
algorithm for the multiple generation of nonlineal mathematical models and
stability study. Advances in Engineering Software. In Press). This allowed the
model to be selected according to the criteria.
Keywords: soybean, photoperiod, temperature, development, modelling,
stability.
... La superficie sembrada con esta oleaginosa ascendió en la Existe cuantiosa información respecto a la ecofisiología de la soja. Es bien conocido el período crítico (fijación del número de granos) para la determinación del rendimiento del cultivo (Jiang y Egli, 1993;Jiang y Egli, 1995;Board y Tan, 1995;Board et al., 1995), el impacto de la elección de la fecha de siembra (Sinclair et al., 1991;Baigorri et al., 2000;Martignone et al., 2006) y las características de las mejores variedades para diferentes zonas del cultivo en el país (Baigorri et al., 2000;Fuentes et al., 2007). Si bien se reconoce el alto impacto del estrés hídrico en las variaciones del rendimiento, aún no se han documentado relaciones funcionales que involucren múltiples variables como para comprender mejor la relación entre el rendimiento de la soja y el balance hídrico agroclimático. ...
... Models of soybean phenology include air temperature and photo-period (Hodges and French, 1985;Sinclair et al., 1991;Jones et al., 1991). Generally, as air temperature rises, the rate of development increases; therefore, higher air temperature results in shorter durations of various stages, such as seed fill. ...
Introduction
Common bean (Phaseolus vulgaris L.) with 20-25% protein and 50-56% carbohydrate content, has a crucial role in supplying the required proteins and maintenance of food security of the community. Among the Asian countries, China, Iran, Japan and Turkey are the major producers of common bean. According to the figures provided by the Ministry of Agriculture, cultivation area and production of bean in Iran in 2016 were 114593 ha and 222705 tones, respectively. In recent years, due to the increase in the population and in order to rapidly meet the demand for more food as well as decision making at micro and macro-levels, simulation of crop growth and yield using the models has gained attention due to rapid preparation of the results, lowering the execution costs and the possibility of simulation under various climatic and management conditions. In order to model the growth stages and yield of bean using the figures of Iranian meteorology organization (minimum and maximum temperatures, radiation and rainfall), a study was conducted at Gorgan University of Agricultural Sciences and Natural Resources. The simple SSM_iCrop2 model was used for this study. This model has been tested and proved for a wide range of plant species. This model requires easily available and limited input information. The aim of this study was to parameterize and evaluate the SSM_iCrop2 model for simulation of growth and yield of common bean in order to investigate the effect of climatic, soil and crop management factors as well as determination of genetic coefficients under Iran conditions using the sub-models associated with phenology, dry matter production and distribution and the changes in leaf area.
Materials and Methods
SSM_iCrop2 model was used as the base of this study. Observed and simulated yield and days to maturity values were compared for parameterization and evaluation of the model. For this purpose, a series of experimental data (data associated with the growth and production of bean and reports from the published and unpublished papers) in major bean cultivation areas of the country were used. First, parameters related to phenology, leaf area, dry matter production, yield formation and water relations were estimated. Then, the model was evaluated using a series of data which were independent from the experimental data used for parameterization. Crop management inputs were also entered according to the experiment reports. For statistical analysis and investigation of model precision in comparison of the data recorded in the previous studies with the data simulated by the model, correlation coefficient (r), root mean square error (RMSE) and coefficient of variation (CV) were calculated and 1:1 diagram was also drawn.
Results and Discussion
In parameterization of SSM_iCrop2 model for bean, the comparison of observed and simulated days to maturity with RMSE, CV and r values of respectively 14 days, 13 percent and 0.76 and comparison of observed and simulated grain yield with RMSE, CV and r values of 62 g m-2, 20 percent and 0.84 indicated the accuracy of the used parameters. Furthermore, in the evaluation stage, RMSE, CV and r values for days to maturity were 8 days, 8 percent and 0.74 and for grain yield were 53 g m-2, 19 percent and 0.77, respectively, which confirms the precision of the model simulation. The model simulated the evapotranspiration of been in a good manner. The values for RMSE, CV and r for the comparison of the observed and simulated evapotranspiration were 63 mm, 11 percent and 0.85, respectively. Application of SSM_iCrop2 model is simple and acceptably precise simulation is possible with minimal parameters and inputs. This model was able to simulate the growth period and yield of bean cultivars in a good manner despite high variations using thermal unit parameters form sowing to harvest, maximum leaf area and maximum harvest index.
Conclusion
Growth and yield of bean was successfully simulated using SSM_iCrop2 model using minimal and available parameters despite different growth habits and high phenotypic and genotypic variations among the cultivars. The results of the model evaluation performed using RMSE, r and CV showed that this model is able to simulate maturity time and grain yield of bean sown in various dates under Iran climatic conditions with a high precision. Thus, due to suitable precision of SSM_iCrop2 model in simulation of bean phenology and yield, it may be used as a suitable tool for investigation of crop systems and interpretation of results under various environmental and management conditions for planning and improving the management of bean fields in the country.
Soybean [Glycine max (L.) Merr.] breeders and geneticists routinely evaluate thousands of plots per year in order to characterize various accessions and breeding populations for a multitude of traits, for example, morphological, physiological, abiotic and biotic stress, plant organs, and seed composition. Most of these trait evaluations require experienced raters to spend countless hours, recording phenotypes for each genotype in different filial generations. Plant breeders strive to work with increased population sizes, and improved accuracy of selection to increase the response to selection. These requirements have motivated the development of high-throughput phenotyping (HTP) methods and associated tools (i.e., hardware) and software solutions. This chapter consists of several topics related to HTP, including sensors, unmanned aerial systems, and ground robots, as these are important components of plant phenotyping in the new technological era. The advances in image-based analysis and machine learning methods have accompanied the improvements in phenotyping capabilities, both aerial and ground. This chapter includes the current state of the art in types of sensors, aerial, and ground-based HTP, in conjunction with machine learning-based analytics, particularly for physiological and morphological traits, abiotic and biotic stresses, and root-related traits. Advances in the integration of HTP with crop modeling are provided. Finally, the complementary relationship between HTP and genomic studies is explained with pertinent examples.KeywordsPhenomicsSensorsUnmanned aerial systemsGround robotsMachine learningCrop modelingPlant stress
Global climate change has several implications on food security. The task of feeding the growing human population with limited resources is a challenging mission. With modern climate-resilient cultivars and optimized management practices, agronomists are trying to provide solutions to optimize the demand and supply balance in the food system. Crop simulation models play a vital role in assessing cultivar’s performances with extrapolated conditions (soil and weather types) and resources (management practices), at varying spatiotemporal scales as large and multilocation field experiments with scarce resources are challenging. New cultivars need to be updated with their genetic coefficients to simulate crop growth and development and hence prediction of phenology and yield under different environments. Most of the modeling studies rely on the calibrated genetic coefficients of crops from different geographical regions and do not calibrate it properly which brings biasness in the model output. Different optimization methods for parameter estimation play a crucial role in meeting these requirements in short period. The selection of methods to estimate genetic coefficients also requires careful cataloguing of input data using standardized and appropriate protocols. With robust estimates of genetic coefficients, the reliability on simulation model will be boosted after proper statistical evaluation on the need of parameterization, testing model symmetry, and improving modeling metrics. Moreover, properly calibrated and validated model can be used to assess crop potential yield analysis, yield gap assessment, projection of climate and economic, and other decision support analysis which helps growers to enhance profitability and strengthen environmental stewardship. In this chapter, we discuss and describe different method of estimating crop genetic coefficients for simulation models. We also highlight advantages and disadvantages of the individual methods with special emphasis on the need of ensembling methods to minimize bias and inherent uncertainties in the estimation of these coefficients. This chapter will provide crop model developers and users an insight over different optimization methods and ensembling needs.KeywordsCrop modelingClimate changeGenetic coefficientOptimizationCalibrationValidation
Improvements in genetics, technology, and agricultural intensification have increased soybean yields, however adverse climate conditions may prevent these gains from being fully realized in the future. Higher growing season temperatures reduce soybean yields in key production regions including the Midwest U.S and better understanding of the developmental and physiological mechanisms that constrain soybean yield under high temperature conditions is needed. This study tested the response of two soybean cultivars to four elevated temperature treatments (+1.7, +2.6, +3.6 and +4.8 oC) in the field over three growing seasons and identified threshold temperatures for response and linear vs. nonlinear trait responses to temperature. Yield declined non-linearly to temperature, with decreases apparent when canopy temperature exceeded 20.9 oC for the locally adapted cultivar and 22.7 oC for a cultivar adapted to more southern locations. While stem node number increased with increasing temperature, leaf area index decreased substantially. Pod production, seed size and harvest index significantly decreased with increasing temperature. The seasonal average temperature of even the mildest treatment exceeded the threshold temperatures for yield loss, emphasizing the importance of improving temperature tolerance in soybean germplasm with intensifying climate change.
One of the greatest challenges for the sustainability of soybean production in Brazil is the control of Asian soybean rust (ASR). This study aimed to assess the climate risk of ASR occurrence throughout the period recommended for sowing in four locations of Rio Grande do Sul, considering different soybean relative maturity groups and the phases of the El Niño–Southern Oscillation phenomenon. The soybean development cycle and the climate favorability for ASR occurrence were simulated for the recommended sowing dates in the period from 1961 to 2020 in four locations of Rio Grande do Sul. Early cultivars, sown at the beginning of the period recommended by Agricultural Zoning of Climate Risk, usually exhibit less favorable climate conditions for ASR occurrence and less interannual variability in their cycle. The effect of the El Nino/Southern Oscillation phenomenon varies across the studied locations, with the prevalence of more favorable conditions for ASR occurrence during El Niño years. However, neutral years show greater interannual variability for the risk of ASR occurrence.
Soybean is the major source of vegetable protein in the world. It is produced over a wide range of latitudes, from the Canadian prairies and Northern Great Plains in the USA to tropical areas in the Brazilian Cerrados. This chapter evaluates the environmental (E) and genetic (G) factors regulating soybean development, growth, seed yield, and seed constituents, as influenced by management (M) practices as well. The chapter focuses on three major soybean-producing regions of the world (Corn Belt in USA, Brazilian Cerrados, and Argentinean Pampas), providing examples about how G × E × M interactions are exploited for regional and local adaptation. Opportunities for yield improvement and future research are highlighted.
Photoperiod response of Indonesian soybean cultivars was investigated in relation to their flowering time. Three parameters which corresponded to basic vegetative growth (BVG), critical optimum photoperioed (Pc) and photoperiod sensitivity (PS) were estimated by hyperbolic or exponential regression of days to flowering (DTF) on the photoperiod. Nine soybean cultivars of Indonesia, as well as a Japanese cultivar as a check, were exposed to a photoperiod of 10 through 14h at 28°C The results showed that the Indonesian soybean cultivars were characterized by shorter Pc and larger BVG value. The estimated parameters were used for predicting DTF under the photoperiod simulating natural daylength at 25° N. Three of the 10 cultivars were sown at intervals of three weeks and grown under the given photoperiod regime. DTF of the three cultivars were predicted with considerable accuracy, especially when the parameters estimated by hyperbolic regression were applied.
This article is part of a series of brief commentaries to highlight papers that have resulted in important and distinctly new perspectives in crop science. A criterion for the selection of papers is that they must have been published at least 20 yr ago to allow for a long-range perspective in assessment of the papers. The current article briefly reviews the paper by George Robertson published in 1968 that provided key concepts used yet today to describe the pace of plant development. Robertson developed the ideas of normalized development rates for each ontogenetic stage, multiplication of temperature and photoperiod responses, and inclusion of base temperature and photoperiod parameters in the response functions. © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA All rights reserved.
The CROPGRO‐Soybean model has not been extensively evaluated in the upper Midwest. The objective of this project was to determine if modifications of the CROPGRO‐Soybean model would improve predictions in the upper Midwest using three cultivars in five management systems and two planting dates from 1997 to 2000. Version 3.5 of the model was compared with 1998 data and found to underestimate total biomass and grain yield at harvest. Changes in temperature function on leaf expansion rate and base temperature for pod addition improved model performance and decreased root mean square error (RMSE) for biomass at harvest and grain yield from 734 to 707 kg ha ⁻¹ and from 410 to 362 kg ha ⁻¹ , respectively. The modified model was then tested with independent data from 1997, 1999, and 2000. Overall, the model parameters calibrated from 1998 data improved the fit slightly but with higher RMSE values for the three independent years than the 1998 data set. Averaged across the 3 yr, the modified model underpredicted biomass at harvest and grain yield by 14 and 6%, respectively, with RMSE for biomass at harvest and grain yield averaging 1181 and 814 kg ha ⁻¹ , respectively. The inaccuracy was related to underprediction of early vegetative growth because of the effect of site‐specific and planting date–specific differences in temperature on biomass accumulation and leaf area index. It was concluded that the modified parameters improved the accuracy of the CROPGRO‐Soybean model for the calibration year but did not significantly improve prediction for the three independent years.
Two distinct soybean (Glycine max L.) cultivars, traditional Essex and newly developed Moon Cake, were grown from emergence to seed filling in controlled climate chambers at two temperature regimes, 26/21°C and 32/27°C (day/night) from April 1 through June 9, 2003. Detailed morphological observations accompanied by photographs were conducted weekly. Analysis of data showed that although both cultivars for both temperatures demonstrated very similar qualitative morphological characteristics, the quantitative difference was significant for each cultivar and temperature regime. Variability of plant height, number of nodes, internode length, and total leaf area surface morphological characteristics were estimated. The morphological variables were described by three- or four-parametric sigmoidal functions of time or stage of development, with parameters varying with temperature and genotype. An open parametric L-systems model to graphically depict growth simulation of the soybean cultivars was developed using the collected data and L-Studio software. The virtual plants were constructed to simulate growth and development of these two soybean cultivars at different temperatures. The L-systems model was the same for all treatments with the effects of temperature and genotype accounted for by L-systems parameters. Partly these parameters were the intrinsic parameters of the L-systems model, the rest of them were the parameters of morphological dependences obtained from the experimental data. The latter were used to depict leaf sizes, branch length, etc, in dynamics of their growth and development. A visual model of soybean can be used for (a) preliminary (visual) validating crop simulation models and (b) as a part of a user interface, and
This study aimed to estimate the large-scale effects of elevated air temperature on the phenological development of soybean by using the agro-meteorological grid data. A logistic model has been developed for describing the developmental rate (DVR) of three soybean cultivars, Ryuhou, Enrei and Fukuyutaka. Root mean square errors of the model estimation ranged from 1.4 to 2.2 days for emergence (VE) to flowering (R2) and 1.2 to 2.8 days for R2 to beginning of seed growth (R5). The model could estimate the effect of elevated air temperature on phenological development in a temperature gradient chamber. The response of DVR to air temperature change from VE to R2 was larger in the medium cultivar, Ryuhou, Enrei, than in the late cultivar, Fukuyutaka. The elevated air temperature simulation (+3°C) resulted in shortened duration of VE to R2 in the cool, early sowing region of eastern Japan by 5 to 7 days, whereas little effect was observed in the warm, late sowing region of Kyushu, in southern Japan. This large influence of elevated air temperature on the phenological development of soybean in eastern Japan was not only because of the use of cultivars sensitive to air temperature change but also because of the range of regional air temperatures, which largely alters the DVR during the cultivation period.
The fourth edition of this important book covers the advances in livestock mineral nutrition, updated with more illustrations and additional material on the relationship between livestock and man. Recent developments are discussed, such as increasing the 'mineral value' of feeds by the use of additives and enhancing mineral availability through the use of organic sources of trace elements. The concept of the 'mineral footprint' of livestock production is introduced and methods of mineral feeding that lower environmental pollution are presented. Opportunities and problems in manipulating the mineral content of livestock to improve the mineral status of consumers are also addressed. The book is an essential resource for researchers and students in animal nutrition, agriculture and veterinary medicine, and a useful reference for those concerned with human nutrition and environmental protection.
Soybean [Glycine mar (L.) Merr.] production systems that require less than 100 d from sowing to maturity are useful for avoiding drought and decreasing irrigation requirement in areas with relatively long growing seasons (> 150 d) and seasonal moisture limitations. Previous experiments have evaluated the response of either maturity group (MG) IV and earlier or MG V and later soybean to increased plant population, but there is a paucity of data quantifying population density responses across the entire range of MGs grown in these areas. Therefore, we evaluated the responses of MG 00, 0, I, II, III, IV, V, and VI soybean to populations of 10, 20, 40, 60, and 100 seeds m(-2) sown in 19-cm rows and irrigated as needed at Fayetteville, AR, in 2001, 2002, and 2003. The response of soybean yield to increased plant populations was described well by an exponential model that predicted an asymptotic yield plateau at high plant populations. Asymptotic yield was similar for MG I through VI cultivars, but plant population required to reach the asymptote generally decreased as soybean maturity lengthened. Harvest index (HI) values generally increased slightly with increased plant population in MG 00 and 0 soybean, decreased slightly with increasing plant populations in MG V and VI soybean, and had no response to increasing plant populations in MG I through IV soybean. Height of first fertile node increased as plant population increased and as soybean maturity lengthened. This research demonstrates that a broad range of soybean MGs can produce similar yield in the Midsouth, but optimal seeding densities and irrigation requirements vary by maturity. Further, this research demonstrates some of the difficulties that can be encountered when expressing soybean yield as an empirical function of soybean population density.
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