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Linear Increase in Soybean Harvest Index during Seed-Filling1
Agronomy Journal
Abstract
Grain-to-stover ratio and harvest index (the ratio of seed mass to total aboveground mass) were studied during soybean [Glycine max (L.) Merr.] seed-filling to determine the potential for using these normalized expressions of yield to improve measurements of the seed-filling period. For example, if either grain-to-stover ratio or harvest index increased linearly during seed-filling, it could be used to calculate an effective filling period. However, calculation of an effective filling period requires a linear approximation. Thus, the specific objective was to determine whether grain-to-stover ratio or harvest index increased as a linear function of time for various genotypes and environments. First, C and N assimilation and distribution rates within plants were simulated. Simulations predicted that harvest index increased linearly throughout seed filling, while grain-tostover ratio increased curvilinearly. Next, simulations were compared with data from field-grown plants at three northern locations. Cultivars included determinate and indeterminate types, and a range from early to late maturities. To a good approximation, harvest index increased linearly during the seed-filling period for all but the early cultivars. The early cultivars approached maximum values of harvest index curvilinearly. Therefore, harvest index has potential for use in calculations which require linear approximations.
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... The use of an evolutive harvest index (equation (14)) was inspired by the works of Sinclair [4,111,149,160]. Thus, in STICS, the final harvest index is sensitive only to the duration of the filling. ...
... For some mechanisms, the choice was made easier by previous works showing that simple formalisms are interesting and have robustness. It was, for example, the case of water balance [94] and the evolutive crop index (works from Sinclair and colleagues [4,111,149,160]). For some other mechanisms the choices were more original (nitrogen balance, raising, leaf area index, crop temperature) and therefore they have to be tested in various experimental situations and through an analysis of sensitivity. ...
... Grain demand for carbohydrate (biomass) is driven using a cultivar-specific daily rate of harvest index (HI) increase (hi_incr) up to a genetic maximum defined by hi_max_pot. Spaeth and Sinclair (1985) were the first to note the constancy of the increase in HI during grain fill in soybean. Since then, the concept has been applied by Sinclair (1986) and Hammer et al. (1995) to simulate grain yield accumulation in soybean and peanut, respectively. ...
... The parameter hi_incr is cultivar-specific to account for known variation in some species (e.g. soybean, Spaeth and Sinclair 1985;peanut, Hammer et al. 1995). Sinclair et al. (1987) use a value of 0.016/day in a model for cowpea. ...
This paper describes the physiological basis and validation of a generic
legume model as it applies to 4 species: chickpea
(Cicer arietinum L.), mungbean
(Vigna radiata (L.) Wilczek), peanut
(Arachis hypogaeaL.), and lucerne
(Medicago sativa L.). For each species, the key
physiological parameters were derived from the literature and our own
experimentation. The model was tested on an independent set of experiments,
predominantly from the tropics and subtropics of Australia, varying in
cultivar, sowing date, water regime (irrigated or dryland), row spacing, and
plant population density. The model is an attempt to simulate crop growth and
development with satisfactory comprehensiveness, without the necessity of
defining a large number of parameters. A generic approach was adopted in
recognition of the common underlying physiology and simulation approaches for
many legume species. Simulation of grain yield explained 77, 81, and
70% of the variance (RMSD = 31, 98, and 46
g/m2) for mungbean (n =
40, observed mean = 123 g/m2), peanut
(n = 30, 421 g/m2), and
chickpea (n = 31, 196
g/m2), respectively. Biomass at maturity was
simulated less accurately, explaining 64, 76, and 71% of the variance
(RMSD = 134, 236, and 125 g/m2) for mungbean,
peanut, and chickpea, respectively. RMSD for biomass in lucerne
(n = 24) was 85 g/m2
with an R2 of 0.55. Simulation
accuracy is similar to that achieved by single-crop models and suggests that
the generic approach offers promise for simulating diverse legume species
without loss of accuracy or physiological rigour.
... The linear change in harvest index (HI) with time has been shown to be a common feature for a number of species, including soybean (Spaeth and Sinclair, 1985), peanut (Hammer et al., 1996;Bennett et al., 1993) and pea (Bindi et al., 1999). De®ning a constant rate of increase in HI is desirable in modelling crop growth and yield, as it has proved effective and robust in a number of modelling efforts including simulations of soybean (Sinclair, 1986). ...
... This study shows that the rate of grain-®lling in pigeonpea could be approximated by a linear increase in HI, reported previously for a number of other crops including the grain legumes soybean (Spaeth and Sinclair, 1985), peanut (Hammer et al., 1996;Bennett et al., 1993) and pea (Bindi et al., 1999). There were clear differences among duration groups in the rate of increase and ®nal value of harvest index. ...
Examining physiological relationships that quantify the processes of interception of radiation and biomass accumulation and partitioning provide one avenue for understanding limits to pigeonpea productivity. The radiation extinction coefficient (k), radiation use efficiency (RUE), partitioning of biomass between leaf and stem before flowering, and the rate of linear increase in harvest index (HI) during pod-filling were determined for nine cultivars in water and nutrient non-limiting conditions at ICRISAT Centre, Patancheru, India. The nine cultivars comprised three each from the cultivar duration classes extra-short (100 days to maturity), short (115 days) and medium (170 days). Values of k and RUE were consistent across duration groups, with mean values of 0.53 and ca. 0.9gMJ−1, respectively. RUE remained at its maximum value almost until maturity. Partitioning between leaf and stem prior to flowering was also consistent across groups, in the ratio of 1:1.03 to 1:1.14. The rate of linear increase in HI and final HI varied across groups, with lower rates of partitioning to grain and final HI in the later maturing groups. When adjusted for fallen leaf, the HI increase was ca. 0.08, 0.075 and 0.04 per day, and maximum HI was ca. 0.35, 0.32 and 0.19 for extra-short, short and medium-duration groups, respectively. The association of lower HI increase with indeterminate growth provides a convenient framework to simulate concurrent reproductive and vegetative growth during pod-filling.
... In contrast, the rate of increase in pod HI from the measured data in our study ranged from 0.008 to 0.0192 pod g g -1 d − 1 across treatments. We compared our results with the literature as shown in Fig. 9 (Ball et al., 2000;Egli 1975;Egli 1993;Egli and Bruening 2007;Egli and Leggett 1973;Egli and Wardlaw 1980;Egli and Zhen-wen 1991;Egli et al., 1978;Egli et al., 1981;Egli et al., 1984;Egli et al., 1989;Gbikpi and Crookston 1981;Kumudini et al., 2001;Meckel et al., 1984;Salado-Navarro et al., 1986;Spaeth and Sinclair 1985;Swank et al., 1987;Thomas et al., 2010;Vega et al., 2001). Based on our literature review, the rate of increase in seed HI from other experimental sites ranged from 0.0100 to 0.0210 g g -1 d − 1 (Fig. 9b). ...
• First soybean multi-model evaluation for simulation of in-season growth dynamics.
• Evidence of high variability in simulated leaf area, reproductive growth, and partitioning.
• Opportunities to improve model processes to resemble soybean reproductive growth.
• Description of processes that can benefit from further model improvement.
• Recommendations for collection of key experimental data for model evaluation.
... Dry matter distribution between vegetative tissues (leaf, stem, tuber), and dry matter re-translocation later in the growing season, allow estimation of harvestable yield (tuber) (Soltani and Sinclair, 2012;Chapter 11). In the current model, tuber formation is simulated based on the linear increase in harvest index concept, which requires the slope of the harvest index increase versus time during the tuber formation period (Spaeth and Sinclair, 1985;A. Soltani et al., 2013). ...
Understanding yield potential (Yp) and yield gap (Yg) in current intensive potato (solanum tuberosum L.) production is essential to meet future food demand with the limited resources. Evaluating yield gap is a strong approach to estimate maximum production potential when all factors are in the best condition. A complete estimation of yield gap and potential yield across all major potato producing regions in Iran is lacking. The global yield gap atlas (GYGA) protocol was used to estimate potential yield of potato in Iran. This protocol is based on the climatic zones (CZs) and the reference weather stations (RWS) buffer zones, soil types in each buffer zone. Thirty-five RWS buffer zones in potato producing regions were selected, and total potato area in the RWS buffer zones covered 83% of the whole potato harvest area. According to the results, the average Y p was 67.3 t ha-1 and actual yield (Y a) was 30 t ha-1. Therefore, the average tuber yield gap was 37.3 t ha-1. These results indicate that the Potato producers achieved 45% of the potential yield in Iran. Iranian farmers produced 5 million tons of potato from about 164,000 ha. If they can obtain only 80% of Y p (53.8 t ha-1), amount of potato production will be 8.8 million tones. As result, they can produce 5.2 million tons tuber yield of potato in 97,000 ha cultivation area. Thus, with closing yield gap and increasing potato production, it is possible to decrease potato lands.
... Dry mass distribution between vegetative tissues and grains, and dry mass re-translocation later in the growing season, allow estimation of harvestable (or economic) yield (Soltani and Sinclair, 2012;Chapter 11). In the current simulations, harvestable yield formation is based on the linear increase in harvest index concept, which requires the slope of the harvest index increase versus time during the yield formation period (PDHI) (Spaeth and Sinclair, 1985;Soltani et al., 2013). Bindi et al. (1997) used the concept in simulating yields of grapes. ...
Crop models are essential in undertaking large scale estimation of crop production of diverse crop species, especially in assessing food availability and climate change impacts. In this study, an existing model (SSM, Simple Simulation Models) was adapted to simulate a large number of plant species including orchard species and perennial forages. Simplification of some methods employed in the original model was necessary to deal with limited data availability for some of the plant species to be simulated. The model requires limited, readily available input information. The simulations account for plant phenology, leaf area development and senes-cence, dry matter accumulation, yield formation, and soil water balance in a daily time step. Parameterization of the model for new crops/cultivars is easy and straightforward. The resultant model (SSM-iCrop2) was para-meterized and tested for more than 30 crop species of Iran using numerous field experiments. Tests showed the model was robust in the predictions of crop yield and water use. Root mean square of error as percentage of observed mean for yield was 18% for grain field crops, 14% for non-grain crops 14% for vegetables and 28% for fruit trees.
... Dry matter distribution between vegetative tissues (leaf, stem, tuber), and dry matter re-translocation later in the growing season, allow estimation of harvestable yield (tuber) (Soltani and Sinclair, 2012;Chapter 11). In the current model, tuber formation is simulated based on the linear increase in harvest index concept, which requires the slope of the harvest index increase versus time during the tuber formation period (Spaeth and Sinclair, 1985;A. Soltani et al., 2013). ...
Understanding yield potential (Yp) and yield gap (Yg) in current intensive potato (solanum tuberosum L.) production is essential to meet future food demand with the limited resources. Evaluating yield gap is a strong approach to estimate maximum production potential when all factors are in the best condition. A complete estimation of yield gap and potential yield across all major potato producing regions in Iran is lacking. The global yield gap atlas (GYGA) protocol was used to estimate potential yield of potato in Iran. This protocol is based on the climatic zones (CZs) and the reference weather stations (RWS) buffer zones, soil types in each buffer zone. Thirty-five RWS buffer zones in potato producing regions were selected, and total potato area in the RWS buffer zones covered 83% of the whole potato harvest area. According to the results, the average Y p was 67.3 t ha-1 and actual yield (Y a) was 30 t ha-1. Therefore, the average tuber yield gap was 37.3 t ha-1. These results indicate that the Potato producers achieved 45% of the potential yield in Iran. Iranian farmers produced 5 million tons of potato from about 164,000 ha. If they can obtain only 80% of Y p (53.8 t ha-1), amount of potato production will be 8.8 million tones. As result, they can produce 5.2 million tons tuber yield of potato in 97,000 ha cultivation area. Thus, with closing yield gap and increasing potato production, it is possible to decrease potato lands.
... Dry mass distribution between vegetative tissues and grains, and dry mass re-translocation later in the growing season, allow estimation of harvestable (or economic) yield (Soltani and Sinclair, 2012;Chapter 11). In the current simulations, harvestable yield formation is based on the linear increase in harvest index concept, which requires the slope of the harvest index increase versus time during the yield formation period (PDHI) (Spaeth and Sinclair, 1985;Soltani et al., 2013). Bindi et al. (1997) used the concept in simulating yields of grapes. ...
Crop models are essential in undertaking large scale estimation of crop production of diverse crop species, especially in assessing food availability and climate change impacts. In this study, an existing model (SSM, Simple Simulation Models) was adapted to simulate a large number of plant species including orchard species and perennial forages. Simplification of some methods employed in the original model was necessary to deal with limited data availability for some of the plant species to be simulated. The model requires limited, readily available input information. The simulations account for plant phenology, leaf area development and senes-cence, dry matter accumulation, yield formation, and soil water balance in a daily time step. Parameterization of the model for new crops/cultivars is easy and straightforward. The resultant model (SSM-iCrop2) was para-meterized and tested for more than 30 crop species of Iran using numerous field experiments. Tests showed the model was robust in the predictions of crop yield and water use. Root mean square of error as percentage of observed mean for yield was 18% for grain field crops, 14% for non-grain crops 14% for vegetables and 28% for fruit trees.
... The stability of the rate of change in reproductive allocation of cotton is comparable to that reported for a number of cultivated species. The average rate of change in reproductive allocation in cotton is % 0.006 d 21 (Sadras, Bange, & Milroy, 1997) much lower than the rates reported for other species, which are range from about % 0.01 to % 0.02 d 21 (production of soybean, Spaeth & Sinclair, 1985;sorghum, Muchow, 1990;wheat, Moot, Jamieson, Henderson, Ford, & Porter, 1996). The low rate of increase in reproductive allocation of cotton, in comparison to cereals, could be related to differences in growth habit: in determinate species there is typically little vegetative growth from shortly after anthesis, while significant vegetative growth is often observed during part of the post flowering period of cotton. ...
Cotton (Gossypium hirsutum L.) is a commercial fiber crop that is being cultivated under diverse climatic conditions across the globe. The demand for cotton and its by-products is increasing day by day due to more consumption in the textile industry and due to source of edible oil. Erratic rainfall patterns and increasing temperature resulting from climate change will likely make drought events more frequent in many regions. There is a growing need to identify and/or bred drought tolerance varieties in order to maintain sustainable cotton production. Drought stress impedes plant growth and development by modifying metabolic pathways. Drought stress decreases the photosynthesis and reduces the supply of photosynthate which result into square and boll shed and low lint yield. Drought affects the number of physiological processes, namely, process of photosynthesis, stomatal regulation and reduction in root-shoot growth, reduced leaf area expansion, reduced transpiration, and osmoregulation. Effect of drought stress mediated through stress sensing/signaling and activation of various parallel stress responses (including physiological, molecular, and biochemical mechanisms) in plants. At the cellular level drought stress stimulates overproduction of reactive oxygen species, mitogen-activated protein kinase, Ca²⁺, and hormone-mediated signaling. Drought induced transcription factors activation of abscisic acid dependent and independent pathways of the stress signaling in cotton. The strategies for improving tolerance against drought stresses may include apart from developing drought-tolerant cultivar, use of primary and secondary nutrients, the use of osmoprotectants, plant growth regulators (PGRs) and the plant growth-promoting rhizobacteria. This review synthesizes the most recent research work on cotton drought responses and the impact of water deficit on lint yield and quality. The current strategy is to move toward integrated approach to combat drought stress.
... The last method is based on the concept developed in soybean by Speath and Sinclair (1985) that HI linearly increases with time over much of the seed growth period. This response has also been observed in sorghum and maize (Muchow, 1988), peanut (Bennett et al., 1993), wheat (Moot et al., 1996), sunflower (Bange et al., 1998), chickpea (Soltani et al., 1999), field pea (Lecoeur and Sinclair, 2001), pigeonpea (Robertson et al., 2001a) and fababeen (Turpin et al., 2002). ...
The linearity of harvest index (HI) increase has been used as a simple means to analyze and predict crop yield in experimental and simulation studies. It has been shown that this approach may introduce significant error in grain yield predictions when applied to diverse environments. This error has been ascribed to variability in the rate of linear increase in HI with time (dHI/dt). Data from two field experiments indicated in chickpea (Cicer arietinum L.) that dHI/dt varied among sowing dates. This variation was related to the length of pre-seed growth phase and vegetative growth (dry matter production) during this phase and could be described by the mean daily temperature from sowing to beginning seed growth. dHI/dt increased linearly with increase in the temperature up to 17 8C when it reached to its maximum value and remained constant. Simulation of these field experiments using a chickpea crop model including a constant dHI/dt resulted in yield over-prediction for some sowing dates. However, a modified HI-based approach greatly improved model predictions. In this approach, potential seed growth rate (SGR) is calculated using the linear HI concept, but actual SGR is limited to current biomass production and the remobilisation of dry matter accumulated in vegetative organs before the seed growth period. This modification well accounted for temperature and drought effects on HI and resulted in better yield predictions under conditions of major chickpea producing areas of Iran. Therefore, we recommend that the modification to be applied in the other HI-based models. #
... Les premiers travaux de modélisation des cultures sont apparus avec la mise en relation des processus instantanés, tels que la photosynthèse et la respiration, avec l'évolution de la biomasse accumulée (de Wit et al., 1970). Par la suite, l'appropriation de concepts existants (Monteith, 1972) et l'émergence de nouveaux concepts (Spaeth et Sinclair, 1985 ;Sinclair, 1986) vont constituer un référentiel pour l'ensemble des modèles de culture. Au Pays-Bas, les travaux de De Wit (1978) et Van Ittersum et al. (2002 ont produit des modèles, dérivés de SUCROS, s'attachant à décrire précisément l'écophysiologie des cultures à des fins souvent didactiques. ...
Il est désormais bien reconnu que les activités agricoles sont à l’origine d’une grande part de la pollution des nappes souterraines par les ions nitrate. Le cas des plaines alluviales est particulièrement intéressant puisqu’elles associent la présence d’un sol riche et profond, très favorable à l’agriculture, et d’une nappe alluviale peu profonde. Dans ce travail nous nous sommes intéressés à deux types de plaine alluviale. La plaine alluviale de la rivière Alegria (Pays-Basque ; Espagne) représente le cas d’une nappe alluviale avec un cours d’eau de faible importance. La recharge de l’aquifère se fait alors principalement par l’infiltration et la percolation de l’eau à travers la zone non saturée du sol. Dans une telle situation les fuites de nitrate sous les parcelles agricoles influencent donc significativement les concentrations en nitrate de la nappe. La modélisation de deux situations culturales (une culture de pommes de terre en 1993 et une culture de betteraves à sucre en 2002) avec le modèle de culture STICS a permis d’une part de confirmer que les pratiques agricoles avaient un impact significatif sur l’évolution des concentrations en nitrate de la nappe, et d’autre part d’expliquer en partie la diminution des concentrations en nitrate de la nappe qui a été observé entre les études (1993 et 2002). La plaine alluviale de la Garonne correspond à une situation ou les concentrations en solutés de la nappe sont influencées par les échanges nappe-zone non saturée mais également par les échanges nappe-rivière. Le couplage des sorties du modèle STICS (drainage et concentration en nitrate) avec le modèle hydro-biogéochimique 2SWEM a permis de rendre compte de ces deux types d’interactions, et ainsi d’une part d’expliquer la répartition spatiale des concentrations en nitrate dans la nappe alluviale, et d’autre part d’évaluer l’impact de modifications des pratiques agricoles sur ces concentrations (notamment l’effet des Cultures Intermédiaires Piège À Nitrate).
... A second alternative, proposed by Spaeth and Sinclair (1985), is to extend the notion of the final harvest index (ratio of grain biomass to total shoot biomass) to the dynamic accumulation of biomass in grains, realizing that a linear variation of the harvest index as a function of time could be assumed. This approach has the advantage of pooling the two sources of assimilates, and is economical in terms of parameters. ...
... Dry mass distribution between vegetative tissues and grains, and dry mass re-translocation later in the growing season, allow estimation of harvestable (or economic) yield (Soltani and Sinclair, 2012;Chapter 11). In the current simulations, harvestable yield formation is based on the linear increase in harvest index concept, which requires the slope of the harvest index increase versus time during the yield formation period (PDHI) (Spaeth and Sinclair, 1985;Soltani et al., 2013). Bindi et al. (1997) used the concept in simulating yields of grapes. ...
... A complete description of SSM was presented by Soltani and Sinclair (2012) and Soltani, Maddah, and Sinclair (2013). The key processes in the model are based on radiation use efficiency and its determinants (Sinclair & Horie, 1989), linkage between dry matter production and transpiration (Tanner & Sinclair, 1983), N limitation in yield formation and resultant leaf senescence (Sinclair & de Wit, 1976), yield formation based on linear increase in harvest index (Spaeth & Sinclair, 1985), and quantifying crop responses to soil water deficit (Sinclair, 2005;Sinclair & Ludlow, 1986). ...
Physiological and management modifications to increase crop yields require an appreciation of the impact of these changes on a geospatial basis. It is quite possible that the yield response to any modification may vary from positive in one location to negative in another location. Therefore, tools to undertake geospatial analysis are required to assess the overall impact. It is argued that mechanistic models based on the physics and physiology of plant development, growth, and yield formation are required for such tasks. Several criteria in selecting model tools are discussed. First, models that need to be “calibrated” are not suited for geospatial assessments because the calibration processes causes the model to be an empirical representation of the calibration data and limited to the calibration environment. Extreme caution is needed to extrapolate model use beyond the domain of calibration, since geospatial analysis requires the model to be run for a range of geographical locations over a number of growing seasons. Second, to readily understand the output from simulations across space and time, models needs to be compact and transparent so output that seems inconsistent or not intuitively obvious can be tracked to the critical features in the model. Models with a smaller number of parameters are likely to be more transparent. Finally, it is necessary that the robustness of the model has been tested against a range of environmental conditions. In this paper, we discuss the example of the Simple Simulation Model (SSM) as an option that meets these criteria.
... A complete description of SSM was presented by Soltani and Sinclair (2012) and Soltani et al. (2013). The key processes in the model are based on radiation use efficiency and its determinants (Sinclair and Horie, 1989), linkage between dry matter production and transpiration (Tanner and Sinclair, 1983), N limitation in yield formation and resultant leaf senescence (Sinclair and de Wit, 1976), yield formation based on linear increase in harvest index (Spaeth and Sinclair, 1985), and quantifying crop responses to soil water deficit (Sinclair and Ludlow, 1986;Sinclair, 2005). ...
... L'adaptation des concepts existants (Monteith, 1972) et l'émergence de nouveaux concepts (Sinclair, 1986 ;Spaeth et Sinclair, 1985) (Williams et al., 1984) est développé pour répondre à des préoccupations agroenvironnementales ; il a ensuite donné naissance à CROPSYST (Stöckle et al., 1994). Au début des années 1990, des modèles à vocation uniquement environnementale apparaissent comme par exemple DAISY et PASTIS (Lafolie, 1991). ...
Abstract
This work is part of the French-Moroccan SudMed project, which was coordinated by University Cadi Ayyad of Marrakech (UCAM) and Center of Space Studies of the Biosphere (CESBIO) in Toulouse. The project also involves many technical partners, amongst which Regional Office of Agricultural Development of the Haouz plain (ORMVAH), which is in charge of agricultural management and dam water distribution for the main cultivated areas on the surrounding of the Marrakech city. The main objective of the project is to develop methodologies allowing the integration of ground data, simulation models, and remote sensing data to evaluate the hydro-ecological resources of semi-arid regions.
In this context, this study aims to use a crop model for simulating grain yield and water consumption of wheat crops in the semi-arid Al Haouz/Marrakech plain. We use the STICS crop model « Simulateur mulTIdisciplinaire pour les Cultures Standard », which was developed by French Agronomic Research National Institute since 1996. It is a dynamic crop model with a daily time step. The specific objectives of this study are :
1- To test STICS model for irrigated wheat crops under semi-arid agro-climatic conditions, especially those of the Haouz plain.
2- To develop a methodology in order to map wheat actual evapotranspiration and grain yield at a regional scale, using the STICS model and remote sensing data.
3- To demonstrate the possibilities offered by STICS in the perspective of improving irrigation water management.
In the first part of this work, the model was calibrated and validated against the data collected in two different semi arid sites : the Yaqui Valley (Sonora, North-West of Mexico) in 1999/2000 and the Haouz Plain during the 2002/2003 and 2003/2004 agricultural seasons. We were particularly interested in the simulation of leaf area index (LAI), a key variable in the processes related to plant growth (photosynthesis, transpiration) which can be derived from remote sensing data. This step of evaluation allowed us to conclude that the STICS model provides accurate estimates of wheat evapotranspiration and grain yield once the leaf area is properly simulated.
In the second part of this study, a preliminary work was performed to compare two methods (forcing and calibration) for controlling the STICS model using time series of LAI derived from remotely-sensed data collected using a handheld radiometer. The analysis was done for three principal variables of interest (LAI, grains yield, and evapotranspiration) through simulations according to the capacities of Earth Observation System to provide data. The analysis was carried out for various frequencies of remote sensing observations (from 1 to 15 days) according to the cloudiness conditions of the Marrakech region. Following this analysis, a methodology was developed to calibrate STICS using ground data and images of LAI derived from high space resolution satellite data (Landsat and SPOT). The methodology was tested over an irrigated area of 9 km² dominated by wheat crops (60% of total surface during the 2002/2003 agricultural season). It made it possible to accurately estimate wheat evapotranspiration at the scale of the irrigated area, but results in unrealistically low space variation of grain yield.
In the last part of this work, we demonstrate that the STICS model can be used as a tool for guiding agricultural practices in the Haouz Plain. The study has revealed the importance of the nitrogen stress effects compared to those of water stress in the studied zone. On the assumption that the plants were growing under optimal conditions of fertilization, the simulations we performed showed how irrigation management can be improve and water can be saved in the Haouz region for the year 2002/2003. Finally, the quantification of water losses highlighted the advantages of early sowing in the region
Keywords : crop model, STICS, wheat, Haouz, leaf area index, grains yield, evapotranspiration, semi arid, irrigation, remote sensing, NDVI.
RESUME
Ce travail a été réalisé dans le cadre du projet franco-marocain SudMed coordonné par l’Université Cadi Ayyad de Marrakech (UCAM) et le Centre d’Etudes Spatiales de la BIOsphère (CESBIO) à Toulouse. Il s’inscrit également dans une coopération avec différents partenaires techniques, dont l’Office Régional de Mise en Valeur Agricole du Haouz (ORMVAH) et l’Agence de Bassin Hydraulique du Tensift (ABHT), responsables de la gestion agricole et de la distribution de l’eau des barrages sur les principaux secteurs cultivés autour de Marrakech. L’objectif principal du projet est de développer des méthodologies permettant d’intégrer les données de terrain, les modèles de processus et les données de télédétection dans le but de comprendre le fonctionnement des régions semi-arides pour évaluer leurs ressources hydro écologiques.
Dans ce contexte, ce travail de thèse avait pour objet l’utilisation d’un modèle de culture pour étudier le rendement et la consommation hydrique de la culture de blé dans la plaine semi-aride de Marrakech/Al Haouz. Le modèle utilisé dans cette étude est le modèle STICS, pour « Simulateur mulTIdisciplinaire pour les Cultures Standard », développé en France à l’INRA d’Avignon depuis 1996. C’est un modèle dynamique de fonctionnement de grandes cultures à pas de temps journalier. Ainsi, les objectifs spécifiques de cette étude sont les suivants :
1- Tester le modèle STICS pour la culture de blé dans les conditions agro-climatiques de la plaine du Haouz.
2- Proposer une méthodologie de spatialisation du modèle pour obtenir des cartes d’évapotranspiration réelle et de rendement à l’échelle d’un secteur irrigué test (quelques km²).
3- Démontrer les possibilités offertes par STICS pour contribuer à l’amélioration de la gestion de l’irrigation des céréales.
Dans la première partie de ce travail, le modèle a été calibré et validé à partir des données collectées dans deux sites différents à climat semi aride : la vallée de Yaqui au Mexique pendant la saison 1999/2000 et la plaine du Haouz pendant les deux saisons agricoles 2002/2003 et 2003/2004. La variable ciblé par le procédé de calibration est l’indice foliaire (surface foliaire par unité de surface au sol), variable clef pour le fonctionnement des plantes (photosynthèse, transpiration) qui peux être observé à partir de données de télédétection optique. Ce travail d’évaluation nous a permis de conclure que le modèle STICS permet de simuler d’une façon acceptable l’évapotranspiration réelle et le rendement du blé une fois que l’indice foliaire est bien simulé.
Dans la deuxième partie de cette étude, un travail préliminaire a été réalisé pour comparer deux méthodes de contrôle (forçage ou étalonnage) du modèle, en utilisant une série temporelle d’observations de l’indice foliaire, estimé à partir de données collectées sur le terrain à l’aide d’un radiomètre portable. L’analyse a été conduite pour chaque variable cible (indice foliaire, rendement et évapotranspiration) et en simulant différentes fréquences d’observations (de 1 à 15 jours) en fonction des conditions d’ennuagement de Marrakech. Ensuite, une méthodologie de contrôle de STICS à partir des données satellitaires à haute résolution spatiale a été proposée pour un secteur irrigué test de 9 km² dédié à la culture de blé (60% de la surface totale). Cette méthodologie a permis de combiner les données terrain et une série de données SPOT et Landsat pour calibrer le modèle STICS. Elle a permis d’estimer d’une façon acceptable l’évapotranspiration du blé à l’échelle du secteur irrigué test. En revanche, les rendements en grain simulés ont présenté une faible variabilité spatiale.
Dans la dernière partie de ce travail, nous avons présenté un exemple d’utilisation de STICS comme outil d’aide à la gestion agricole dans la plaine du Haouz. Nous avons utilisé le modèle pour critiquer les itinéraires techniques de parcelles à semis précoce et tardif. L’étude révèle l’importance du stress azoté par rapport au stress hydrique dans la zone étudiée. Dans les conditions non limitantes d’azote, les simulations du pilotage d’irrigation du blé par STICS ont mis en évidence les avantages du semis précoce dans la région du Haouz. Enfin, cette étude nous a permis de quantifier les pertes d’eau associées au mode de gestion de l’irrigation.
Mots clefs : modèle de culture, STICS, blé, Haouz, indice foliaire, rendement, évapotranspiration, semi aride, irrigation, télédétection optique, NDVI.
... ) _ _ ( , 1985;Muchow, 1988Muchow, y 1990Hammer et al., 1995;Moot et al., 1996;Bange et al., 1998;Vega et al., 2000). Así, el uso del modelo lineal es lo suficientemente simple para usarse en los modelos de los cultivos ( Hammer et al., 1995). ...
To estimate the production of aerial biomass of crops using remote sensing, the production of biomass and spectral data in five crops measured in the field were analyzed during a sampling campaign in the Valle del Yaqui, Sonora. The spectral information was processed to obtain the vegetation iso-soil index (IVIS). Multitemporal analyses show a similar response between crop development and IVIS, and a linear relationship was obtained between them. Similarly, the harvest index and its relationship with IVIS were analyzed. The results showed that IVIS is a suitable index for estimating biomass and yield in crops.
... The effect of CO 2 concentration on radiation use efficiency is expressed using an exponential function adapted from Stockle et al. (1992), and is included in the STICS model (Bergez et al. 2014). The seed yield formation is calculated by applying a harvest index increasing linearly with days after flowering (Spaeth and Sinclair 1985) and shoot biomass. Threshold temperatures for translocation can temporarily halt seed filling. ...
Climate change is projected to increase growing season length and temperature in Canada but how soybean [Glycine max (L.) Merr.] will respond is uncertain. By modelling soybean responses to climate change scenarios, stakeholders can develop adaptation strategies. The CSM-CROPGRO-Soybean and STICS models were used to simulate soybean responses under baseline (1971–2000) and in near (2041–2070) and distant (2071–2100) future climate scenarios, including those resulting in altered seeding dates in eastern Canada. Field data collected in Ottawa were used to evaluate the models. The simulated seed yield using the CSM-CROPGRO-Soybean model showed an increase of about 14% (0.34 t ha−1) in the near future and a decrease in the distant future under RCP8.5 and the STICS model estimated a decrease in both the near and distant future. When the crop parameters determining the life cycle were increased by 30% and 40%, the simulated seed yield increased by more than 5%–10% and 10%–20% and by more than 20%–30% and 27%–40% if combined with current harvest index levels. Our simulations showed that soybean seed yield would not benefit from a prolonged growing season under the projected future climate in eastern Canada, unless harvest index is maintained.
... Rather, differences in HI at maturity reflected differences in the length of the effective grain-filling period due to variable N supply. Spaeth and Sinclair (1985) found that the HI of a wide range of soybean genotypes increased linearly for most of the grain-filling period. They suggested that this response should allow the calculation of an effective grainfilling period from measurements of final HI, the slope of the HI increase during grain-filling, and the intercept on the time axis (defined as the beginning of effective grain-filling). ...
Soil microbial communities are often overlooked in the context of ecological restoration. Given their central role in a broad range of ecosystem processes, however, understanding their response to restoration activities is critical to predicting restoration trajectories. In this study, we quantified the response of soil bacterial and fungal communities to restoration treatments, variation in microhabitat elements and vegetation in a critically endangered Australian box-gum grassy woodland ecosystem. Restoration treatments included the addition of coarse woody debris (CWD) and reduced grazing pressure. Four years after applying restoration treatments, we found no significant effect of CWD addition on soil microbial diversity, while reduced grazing significantly affected composition of the fungal, but not the bacterial, communities. Both bacterial and fungal communities responded to microhabitat element (open ground vs. old logs and trees), overlying vegetation and soil edaphic properties, and strong aboveground-belowground linkages were observed. Plant alpha diversity was positively correlated to soil bacterial, but not fungal, alpha diversity and plant community composition was a good predictor of both soil bacterial and fungal beta diversity. Co-occurrence network analysis identified numerous complex, non-linear associations between soil bacteria, fungi, edaphic properties and overlying plants. Soil microbes affected by restoration treatments included fungal saprotrophs and Actinobacteria, likely involved in litter breakdown, as well as bacteria likely involved in soil N cycling. Although the directions of the observed plant-microbe relationships remain unclear, we demonstrated the possibility of inducing changes to soil microbial communities to enhance restoration outcomes in box-gum grassy woodland ecosystems.
... A fraction of stem and pod wall mass (0.25 and 0.30, respectively), at the start of grain filling is deemed potentially available for grain filling (Diepenbrock 2000), if demand exceeds the supply of assimilate from current photosynthesis. The demand for assimilate for grain yield accumulation is defined by the linear increase with time of the harvest index, first noted as a phenomenon in soybean (Spaeth and Sinclair 1985) and since then described in many species including peanut (Hammer et al. 1995), maize and sorghum (Muchow 1988), pigeonpea and wheat (Moot et al. 1996). Such a simple approach avoids the complex interactions and trade-offs between grain yield components as summarised by Diepenbrock (2000). ...
The canola (Brassica napus L.) module in the Agricultural Production Systems Simulator (APSIM) was developed in the late 1990s. There has been no peer-reviewed account of the scientific underpinnings of the module, despite considerable testing across a wide range of environments in the Australian grains industry and numerous applications of the model to address agronomic and crop adaptation issues. This paper presents a summary of the parameters in the module and reviews the physiological evidence justifying their values and module performance, and reflects on areas of module improvement and application. APSIM-Canola simulates crop development, growth, yield and nitrogen (N) accumulation in response to temperature, photoperiod, radiation, soil water and N supply, with a daily time-step, using well-accepted approaches. The module has been validated on more than 250 data points across Australia, China, and Germany and typical root mean squared deviations for days to flowering are ∼5 days and for grain yield are ∼0.4tha-1. Testing on vernalisation-responsive winter types and in high yielding situations has indicated that more research is required to define phenology parameters and yield forming processes in high yielding environments. There is a need to develop better predictive routines for grain oil content that take account of the dynamics of grain filling and interactions with environmental conditions, and improve upon current regression-type approaches. Further testing of N responses is required. Physiological characterisation of new cultivar types, such as hybrids, Indian mustard (Brassica juncea), and new herbicide tolerance types is required to make the module more applicable to contemporary canola production systems. A lack of understanding of the effects of high and low temperature extremes on reproductive processes is currently limiting the use of the module outside conventional sowing dates and agro-climatic zones.
... La biomasa aérea reproductiva se puede referir a granos o frutos solamente o, sin perdida de generalidad, referirse a los órganos reproductivos del cultivo (flores, vainas, granos, frutos). ) _ _ (, 1985; Muchow, 1988 Muchow, y 1990 Hammer et al., 1995; Moot et al., 1996; Bange et al., 1998; Vega et al., 2000). Así, el uso del modelo lineal es lo suficientemente simple para usarse en los modelos de los cultivos (Hammer et al., 1995). ...
RESUMEN Para estimar la producción de biomasa aérea en cultivos utilizando sensores remotos se realizaron análisis de la producción de biomasa e información espectral en cinco cultivos medidos en campo, durante una campaña de muestreo en el Valle del Yaqui, Sonora. La información espectral fue procesada hasta la obtención del índice de vegetación iso-suelo (IVIS). Los análisis multitemporales muestran un comportamiento similar entre el desarrollo de los cultivos y el valor del IVIS, por lo que se obtuvo una relación lineal entre ellos. De manera similar fueron analizados el índice de cosecha y su relación con el IVIS. Los resultados mostraron que el IVIS es un índice adecuado para la estimación de biomasa y rendimiento en cultivos. Palabras clave: biomasa, índice de cosecha, índice de vegetación, estimación de rendimientos. SUMMARY To estimate the production of aerial biomass of crops using remote sensing, the production of biomass and spectral data in five crops measured in the field were analyzed during a sampling campaign in the Valle del Yaqui, Sonora. The spectral information was processed to obtain the vegetation iso-soil index (IVIS). Multitemporal analyses show a similar response between crop development and IVIS, and a linear relationship was obtained between them. Similarly, the harvest index and its relationship with IVIS were analyzed. The results showed that IVIS is a suitable index for estimating biomass and yield in crops.
... The DMAC was defined as the slope of the linear increase in HI during seedfill (Salado-Navarro et al., 1985a). As such, DMAC has been suggested as an alternative measure of the ISGR as a representation of the whole-plant seed growth rate (Spaeth and Sinclair, 1985). ...
... grains, fruits or vegetative storage organs such as stems or roots). Two approaches are possible: i) a source/sink approach for species that continue growing leaves while producing harvested organs (STICS accounts for this trophic stress, which limits fruit setting and filling and leaf surface growth) or ii) a dynamic harvest index, equal to the ratio of grain biomass to total shoot biomass (Spaeth and Sinclair, 1985), allowing dynamic accumulation of biomass in harvested organs and storage of C from leaf assimilates within a perennial organ. A linear variation of the harvest index as a function of time or thermal time is thus calculated. ...
... With indeterminate crops such as fababean, partitioning of assimilates to grain during the grain-filling stage needs to accurately reflect the incrementally increasing demands of new flowers and fruits. One approach advocated for the analysis of partitioning of biomass to grain is the linear increase in HI during grain-filling (Spaeth and Sinclair 1985) in which assimilate demand is increased each day of grain-fill by a set fraction (HI increase, HI incr ). The HI approach is particularly attractive for indeterminate legumes because the alternative of applying a seed growth rate to a predicted number of seeds presents difficulties with species in which seed number is constantly changing during the podset period. ...
The growth and yield of fababean (Vicia faba) in
temperate environments has been well described; however, information is
lacking on the response of the crop to the higher temperature and radiation
conditions of subtropical regions. Our aim in this study was to quantify
fababean canopy development, radiation interception, radiation use efficiency,
biomass partitioning, and nitrogen (N) accumulation and partitioning in a
subtropical winter environment and to investigate if parameters describing
these processes were consistent between temperate and subtropical regions. Two
of the most important factors effecting growth patterns and yield in the field
are crop density and water supply. Thus, 2 field experiments were conducted at
Lawes, south-eastern Queensland, over 2 seasons, the first concentrating on
the effect of plant density and the second on varying water deficit, both
using the widely adapted cv. Fiord.
Main-stem nodes appeared at the rate of one node every 54 degree-days (base
temperature 0˚C), with no effect of plant density. With the addition of
each main-stem node, plants produced a constant 5.22 leaves per node until the
start of grain-filling, at which time assimilate became limiting. High plant
density decreased both the number of leaves produced and the size of
individual leaves on later formed branches. Radiation use efficiency values of
1.03–1.29 g/MJ were determined for plants grown under well-watered
conditions, with a lower value (0.83 g/MJ) for a partly irrigated crop.
The measured radiation extinction coefficient was 0.73 for leaf area index
values ranging from 0.4 to 7.5, pooled across experiments and treatments. Leaf
and stem were partitioned in equal proportions until pod set, and the root :
shoot ratio was c. 0.8 at the beginning of pod set. The rate of increase in
harvest index (HI) during pod filling was 0.012/day, except under fully
irrigated conditions in 1999, when HI was much reduced, possibly due to pod
shedding.
Parameters such as the extinction coefficient, partitioning between leaf and
stem, and rate of main-stem node appearance appeared to be quite conservative
in response to density and water deficit, and were within the range of
published values from temperate and Mediterranean environments. This is an
encouraging outcome and suggests that it should be possible to simulate growth
and yield of fababean across the diverse climate zones in which the crop is
grown in Australia by using a single simulation model.
... Grain yield (GY) is simulated as the product of harvest index (HI) and DM. Spaeth and Sinclair (1985) reported that HI of soybean increased linearly with time throughout grain filling. Chapman et al. (1993) used this concept to simulate HI of sunflower via a relationship between HI and days after sowing. ...
A study was undertaken to identify improved management strategies for barley (Hordeum vulgare L.), particularly in relation to time of planting, location, and frost risk in the variable climate of north-eastern Australia. To achieve this objective, a crop growth simulation model (QBAR) was constructed to integrate the understanding, gained from field experiments, of the dynamics of crop growth as influenced by soil moisture and environmental variables. QBAR simulates the growth and yield potential of barley grown under optimal nutrient supply, in the absence of pests, diseases, and weeds. Genotypic variables have been determined for 4 cultivars commonly grown in the northern cereal production areas. Simulations were conducted using long-term weather data to generate the probabilistic yield outcome of cv. Grimmet for a range of times of planting at 10 locations in the north-eastern Australian grain belt. The study indicated that the common planting times used by growers could be too late under certain circumstances to gain full yield potential. Further applications of QBAR to generating information suitable for crop management decision support packages and crop yield forecasting are discussed.
... Considering only the first flush of pods in Satin, the HI was 0.40. Interestingly, the linear increase in HI reported by Spaeth and Sinclair (1985) for soybean was observed here over only part of the reproductive period in mungbean (Fig. 4). The numbers of pods plant-' and seeds plant-1 produced by each genotype increased in the order Satin < CPI 106032 < ACC 41 (Table 3). ...
The accumulation and partitioning of nitrogen (N) and dry matter (DM) by mungbean (Vigna radiata (L.) Wilczek) were examined in a glasshouse experiment using three contrasting genotypes: a wild accession, a landrace cultivar and a recent commercial cultivar developed for mechanized agriculture. There was genotypic variation for all characteristics investigated. The landrace line accumulated N and DM at greater rates and to greater amounts than either of the other genotypes. However, yields of seed DM and N depended more on the harvest index (HI) and nitrogen harvest index (NHI) than on total biomass and total N accumulation, and both HI and NHI ranked commercial cultivar > wild genotype > landrace line. The three genotypes exhibited differing strategies for the partitioning of assimilates to seed. Seed production in the commercial cultivar occurred in two distinct flushes, with the immediate products of fixation and photosynthesis adequate to provide most of the requirements for N and DM for seed from the first flush. Seed from the second flush derived c. 37% of the N and 31% of the DM from remobilization of vegetative reserves. By contrast, 90% and 42% of seed N and DM respectively in the landrace line came from remobilization. The wild genotype was intermediate in that seed assimilates were concurrently derived from both N2 fixation, photosynthesis and remobilization. The implications for adaptation and further improvement in productivity through breeding are discussed.
... The accumulation of mass by soybean (Glycine max Mer.) seeds, which was discovered to be readily described by a linear increase in harvest index (Spaeth and Sinclair, 1985), has been subsequently observed in a number of species and conditions (Bindi et al., 1999), including ®eld pea (Pisum sativum L.) (Lecoeur and Sinclair, 2001b). This approach has been extrapolated to examine the increase in nitrogen harvest index (NHI) during seed-®ll. ...
A linear increase in nitrogen harvest index (NHI), especially when expressed on the basis of cumulative thermal units, has been observed to be a common feature during seed growth in field pea (Pisumsativum L.). The linear increase in NHI was observed even though the individual variables defining plant N uptake and partitioning within the plant changed during seed-fill and were highly variable across a range of growing conditions. A simulation analysis was undertaken in an effort to reconcile the observed linearity in NHI increase when there is variability in the input variables. A wide range of conditions was used including low plant density, water deficit, high-temperature stress and N soil deficiency that resulted in final amounts of accumulated nitrogen ranging from 2.2 to 26.5gm−2. The simulations confirmed the linearity in NHI increase, although the values of the NHI increase varied considerably among tests as observed in the experimental results. The explanation for the observed linearity of NHI increase resides in the dominance of plant N uptake over seed N accumulation combined with N transfer from the vegetative tissue to the seed during the early stages of seed growth. Simulations revealed that a reduction in rates of increase of NHI between early and late stages of seed growth had little impact on the overall linear behaviour of NHI increase for the entire period of seed-fill. The analysis also highlighted the importance of total plant N accumulation in influencing grain yield in field pea, especially with regard to the stability of final NHI with an average value of 0.79±0.02 (p
... Using the second approach involving the calculation of HI increase, Spaeth and Sinclair (1984) showed that normalisation of fi nal seed yield can decrease problems due to large uncertainty in measurements of fi eld-grown plants. Spaeth and Sinclair (1985) found that seed growth in soybean may be better defi ned by determining the change of harvest index with time during the seed-growth period. They found that HI increased linearly over much of the seed-development period and that the rate of increase of HI (dHI/dt) was almost constant for most soybean cultivars. ...
Crop growth simulation models have been mainly developed to simulate final yield reliably. Thus, a main challenge in these models is the definition of a stable method for expressing the growth of harvested organs (e.g., fruit, seed, tuber, etc.). Generally, two approaches have been used: growth rate analysis of harvested organs [yield growth rate (YGR)] and analysis of harvest index (HI) increase over time (dHI/dt). This work aims to: 1) examine whether YGR and dHI/dt increase linearly over much of growing period, and 2) compare the two growth indices in terms of stability across a number of treatments, in order to identify which is the best indicator of harvest-organ growth. This analysis has already been performed fora large number of field crops, including wheat (Triticum aestivum L.), sunflower (Helianthus annuus L.), soybean (Glycine max L. (Merr.) and pea (Pisum sativum L.), but it has never been attempted in crops where final yield is not simply seeds. In this study, YGR and dHI/dt performances for tomato (Lycopersicum esculentum Mill.), potato (Solanum tuberosum L.), and eggplant (Solanum melongena L.) were compared using 21, 18, and 4 datasets, respectively. Results indicated that both descriptors of harvest-organ growth increased linearly for most of the growth period, whilst the comparison among the two variables in terms of stability showed that, although a direct statistical test failed, dHI/dt was more suitable to describe harvest-organ growth (smaller coefficient of variability) under a large range of crop management conditions (e.g., irrigation, sowing date, planting density, and water salt concentration).
... Using the second approach involving the calculation of HI increase, Spaeth and Sinclair (1984) showed that normalisation of fi nal seed yield can decrease problems due to large uncertainty in measurements of fi eld-grown plants. Spaeth and Sinclair (1985) found that seed growth in soybean may be better defi ned by determining the change of harvest index with time during the seed-growth period. They found that HI increased linearly over much of the seed-development period and that the rate of increase of HI (dHI/dt) was almost constant for most soybean cultivars. ...
... Daily grain growth rate was calculated based on the empirical observations that the harvest index (HI) of crops increases linearly during seed growth (Saaldo-Navarro, 1995;Spaeth and Sinclair, 1985;Muchov, 1990;Hall et al., 1990). Field data collected in experiment 1 during grain growth was used to calculate the linear increase in HI during grain growth period (Figure 3). ...
RÉSUMÉ
Le tournesol (Helianthus annuus L.) est l’une des cultures oléagineuses les plus importantes; il se cultive assez souvent dans des régions de sécheresse où les variations dans les précipitations rendent la production agricole risquée et où les cultures subissent un stress dû au manque d’humidité au cours de différentes phases de leur croissance. Pour pouvoir évaluer les risques de production, on utilise des modèles de culture. Cet article décrit la croissance et l’évaluation d’un modèle combiné empirique et mécanique du tournesol. Ce modèle utilise quelques rapports conservateurs pour définir le développement de la surface feuillue comme fonction du nombre de feuilles et le nombre de feuilles comme fonction d’accumulation d’unités thermiques. L’accumulation de la biomasse est simulée comme fonction de fraction d’assimilation photosynthétique de l’énergie lumineuse et d’efficacité d’utilisation de l’énergie lumineuse. La croissance de la semence est simulée de l’accroissement linéaire de l’index de récolte au cours du temps. Le modèle a été calibré empiriquement pour que soient prédits la croissance et le rendement du tournesol sous l’effet de l’irrigation et les effets du stress dû au manque d’humidité à l’aide du développement de facteurs de sensibilité correspondants. Le modèle a prévu de façon satisfaisante la biomasse aérienne, la surface feuillue et le rendement final du tournesol sous l’effet de l’irrigation et du stress dû au manque d’humidité.
An approach based on a linear rate of increase in harvest index (HI) with time after anthesis has been used as a simple means to predict grain growth and yield in many crop simulation models. When applied to diverse situations, however, this approach has been found to introduce significant error in grain yield predictions. Accordingly, this study was undertaken to examine the stability of the HI approach for yield prediction in sorghum [ Sorghum bicolor (L.) Moench]. Four field experiments were conducted under nonlimiting water and N conditions. The experiments were sown at times that ensured a broad range in temperature and radiation conditions. Treatments consisted of two population densities and three genotypes varying in maturity. Frequent sequential harvests were used to monitor crop growth, yield, and the dynamics of HI. Experiments varied greatly in yield and final HI. There was also a tendency for lower HI with later maturity. Harvest index dynamics also varied among experiments and, to a lesser extent, among treatments within experiments. The variation was associated mostly with the linear rate of increase in HI and timing of cessation of that increase. The average rate of HI increase was 0.0198 d ⁻¹ , but this was reduced considerably (0.0147) in one experiment that matured in cool conditions. The variations found in HI dynamics could be largely explained by differences in assimilation during grain filling and remobilization of preanthesis assimilate. We concluded that this level of variation in HI dynamics limited the general applicability of the HI approach in yield prediction and suggested a potential alternative for testing.
The study reports the simple procedure for reducing the cratering defect of water-based paint using natural dyes. The dyes were extracted from Caesalpinia sappan wood using a water solvent at a temperature of 80°C. Then the solution was spray-dried at a temperature of 110°C and the powder was trapped and filtered. The powder dye was then mixed with the paint constituents using a magnetic stirrer. Various concentration of antifoam ranging from 0 to 5% was used for controlling the crater defects. The results show that antifoam can effectively reduce the crater defect in water-based paint using natural dyes. Some quality assessments of the natural painting were also investigated and show that the water-based paint using Caesalpinia Sappan dye fulfilled the quality standard from Indonesia National Standard and therefore can be developed into mass production.
An agricultural system is a complex combination of many different components that require different types of data for analysis and modeling. Remote sensing information is an alternative source of data for areas that only have a small amount of ground truth data. The goal of this study was to evaluate whether remotely sensed data can be used for calibration of genetic specific parameters (GSPs) with the ultimate goal of yield estimation. This study used the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) with measured Leaf Area Index (LAI) for soybean fields in Paraná, Brazil and Iowa, USA, to calibrate the cultivar parameters of the CSM-CROPGRO-Soybean model. Three calibration methods were performed including field-measured LAI, remotely sensed derived LAI, and remotely sensed derived Light Interception. The cultivar parameters sensitive to LAI and LI were calibrated for yield with a mean error of -4.5 kg/ha (0.1%) and with a R2 of 0.89 for Parana. The availability of crop growth measurements for Iowa resulted in an average RMSE of 895 kg/ha (average nRMSE of 6%), and Willmott agreement index of 0.98 for time-series biomass, and an average RMSE of 941 kg/ha (average nRMSE of 15%) for pod weight. This study showed that remotely sensed LAI and LI from NDVI data can be used for calibration of GSPs with the ultimate goal of improving yield predictions based on local dynamic temporal and spatial variability.
The goal of this study was to investigate climatic factors, crop management and plant growth traits, using
a simple model to simulate growth and yield of soybean in Tehran climatic condition in order to be use as
an efficient tool to study effective factors on yield. The model was evaluated under Tehran condition.
Soybean is one of the important resources of herbal oil and protein. At the current study, production and
partitioning of dry matter in vegetative parts and grains of soybean were investigated by changing the
parameters of base temperature, the maximum leaf area index and the fraction of crop total dry matter at
the beginning of grain growth. Decreasing and increasing of base temperature caused to decrease and
increase the phenological stages, changing the production of dry matter and the allocation of dry matter to
vegetative organs and grains. Decreasing and increasing of the fraction of remobilized dry matter to
grains lead to enhance and reduce harvest index, respectively. To model evaluation, some statistics on
differences between observed and simulated values were used. The simulated yield ranges were within
1890-2220 kg.ha-1 with mean values of 1982.5 kg.ha-1 and the observed yield ranges were 1632-2254
kg.ha-1 with mean values of 2014.5 kg.ha-1. The RMSE was 178.48 kg.ha-1 that was equal to 9% of
average of observed and predicted values. All data points were included at the 80% of 1:1 line and it
indicated that the model had suitable accuracy to predict soybean yield in Tehran climatic condition.
The concept of a linear increase in harvest index, dHI/dt, has proven very useful for crop simulation modeling. The effect of high temperature on the response of dHI/dt of pods and seeds of peanut (Arachis hypogaea L.) has not been described. The objectives of this work were to determine (i) whether dHI/dt was linear at high temperature, (ii) whether high temperature affected dHI/dt and/or the timing of the linear phase of increase in HI, and (iii) whether there was genotypic variation in the response of dHI/dt to high temperature. Four peanut genotypes varying in heat tolerance were grown in pots at either 28/22 or 38/22°C from 21 to 90 d after planting (DAP). Plants were harvested on 10 occasions starting 27 DAP and total dry matter accumulation and partitioning measured. High temperature reduced total dry weight by 20 to 35%, seed HI by 0 to 65%, and seed dry weight by 23 to 78%. At 28/22°C, dHI/dt for pods and seeds was linear and varied from 0.0058 to 0.0109 d⁻¹ At 38/22°C, dHI/dt of pods and seeds was also linear and varied from 0.0028 to 0.0089 d⁻¹ There were genotypic differences in response to temperature. High temperature had no effect on dHI/dt in moderately tolerant genotypes 796 and 47‐16. In susceptible genotypes ICGV 86016 and ICGV 87282, however, the start of pod and seed filling was delayed by 5 to 9 d and dHI/dt reduced by 20 to 65% at 38/22°C. Reductions in pod and seed dry weight at 38/22°C were therefore due to reductions in total dry matter and dHI/dt, depending on the heat tolerance of the genotype. Crop models need to account for genotypic differences in the response of timing and rate of dHI/dt to high temperature to successfully simulate yields in warmer environments.
The relationship between seed and shoot dry matter increase (R s/w ) during the seed-filling period (SFP) can reflect the balance between the assimilate supply and the sink capacity of harvest organs. In a 2-yr experiment, the R s/w in determinate soybean [Glycine max (L.) Merr.] during SFP was investigated under various growing conditions induced by shading and thinning for plants grown under both standard and reduced densities. The R s/w approximated a positive linear regression in both experiments. Slopes of the lines were less than 0.5 and did not vary within the same year even if plant density was reduced. The shoot dry matter increase (W) hence seemed to directly determine seed dry matter increase (S). A positive relationship between total pod number and W or S was observed in both years. Only minimal changes in seed numbers per pod and individual seed weight were observed, regardless of the treatment applied. A positive relationship also existed between total pod number and the number of branch nodes, and hence W increased S via increase in the number of branch node bearing pods. Half the current assimilate product during SFP appeared to be used for increase of vegetative plant parts. This determined total pod number and hence potential S. Determinate soybean is thus very different from cereal crops, in which almost all the current assimilate during the SFP is appropriated to S. The stability of the harvest index in soybean reflects the important effect of the W on pod establishment during SFP.
Breast cancer is a cancer that attacks women generally and with the highest ratings in Indonesia. Human estrogen receptor alpha (ERa) is one of the most studied targets for in silico screening of bioactive compounds. The estrogenic activity of a vast number of chemicals has been studied for their potentially adverse effects on the hormone regulation of the endocrine system. The objective of this study was to evaluate the esterogen alpha agonist activity of certain natural pigments using in silico docking studies. In this perspective, β-carotene, lycopene, lutein, zeaxanthin, fucoxanthin, pheophytin a, and pheophytin b were prepared for the docking evaluation. In silico docking studies were carried out using recent version of AutoDock Vina embedded in PyRx version 8.0. The docking results were observed as free energy of binding (Gbind) and the selected docking pose was visualised using Pymol. The results showed that the selected natural pigments binding energy
ranging between -5.07 kcal/mol to -7.63 kcal/mol with the highest ligant binding energy is lutein.
On one view, a search for critical physiological traits merely needs to consider those factors that may determine the production of assimilate by the leaves; yield is then regarded as the product of the leaves’ activities. The alternative view is that yield is not an amorphous ‘sink’ but a structured population of ovules. The search for critical physiological traits then becomes a search for factors that control population structure; i.e. that control the ‘birth’ and ‘death’ of ovules. The physiology of plant control systems is discussed with particular reference to the cool season legumes.
This paper considers how and the extent to which climate change and the balance of nitrogen between crops and soils may interact and how the ability to modify genetically specific crop attributes might affect the overall nitrogen balance of the crop-soil system. The possible effects of each change have been assessed using the AFRCWHEAT2 crop model for wheat. Model output pointed to a decrease in harvest index as a result of coincidental increases in CO2 level and temperature, and the importance of considering not only changes to average but also to the variability of environmental driving variables is illustrated. When mean temperatures were raised the model predicted that more nitrate would be left in the soil at the end of the season but that raising CO2 level could counter this effect. Doubling the variability of temperature had a more complicated effect on the soil N balance with the mean amount of residual soil nitrate predicted to be at about the same level as for the baseline and with only a comparatively small change in its coefficient of variation. These results are interpreted in terms of a bell-shaped response of mineralisation rate to temperature.
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.
La naissance des modèles de culture correspond à une valorisation des travaux sur la photosynthèse et la respiration (de Wit et al., 1970) avec une mise en relation entre la biomasse accumulée et les processus instantanés. Par la suite, on assiste à l'appropriation de concepts existants (Monteith, 1972) et à l'émergence de nouveaux concepts (Spaeth et Sinclair, 1985) qui vont constituer un référentiel pour l'ensemble des modèles de culture. L'école hollandaise (van Ittersum et al., 2002) a produit des modèles, dérivés de SUCROS, s'attachant à décrire précisément l'écophysiologie des cultures à des fins souvent didactiques. L'école américaine, avec les modèles GOSSYM / GLICYM (Whisler et al., 1986, McKinion et al., 1988), CERES (Ritchie et Otter, 1985) dont dérive le modèle australien APSIM (McCown et al. 1996) et la famille de modèles CROPGRO (Boote et al., 1998, Jones et al., 2002), s'est approchée d'objectifs agronomiques et a commencé à introduire les pratiques agricoles. Dans le même temps, EPIC (Williams et al., 1984), le premier modèle générique, est développé pour répondre à des préoccupations agro-environnementales ; il a donné naissance à CROPSYST (Stockle et al., 1994). Au début des années 1990, des modèles à vocation uniquement environnementale apparaissent comme par exemple DAISY (Hansen et al., 1990). La tendance actuelle est à la mise au point de modèles génériques et agro-environnementaux, qui prennent en compte l'effet des pratiques : c'est le cas de STICS (Brisson et al., 1998, 2003, 2009), modèle développé à l'INRA depuis 1996. B. Les principes des modèles de culture L'ensemble sol-couvert végétal constitue le système modélisé. L'atmosphère au voisinage du système est en général appréhendée comme la condition limite supérieure et représentée par un ensemble de variables climatiques issues de mesures standard (stations météorologiques d'un réseau) ayant une fonction de forçage. En termes fonctionnels, ces modèles se structurent autour d'un schéma dynamique, au pas de temps journalier, de croissance du couvert végétal reposant sur le fonctionnement carboné des plantes. Le rayonnement intercepté par l'appareil photosynthétiquement actif est transformé en biomasse qui, selon les modes de représentation du système, est répartie dans différents types d'organes. Cette répartition des assimilats repose sur des proportions préétablies en fonction des stades de développement ou sur des équilibres sources – puits. Dans tous les modèles de culture, c'est le flux de matière carbonée qui pilote le fonctionnement trophique de la plante. Il est accompagné, dans certains cas, d'un flux d'azote pouvant également être soumis à une loi de répartition entre organes. L'essentiel des autres processus est introduit dans les modèles comme des flux d'information servant à orienter, modifier ou limiter le flux de carbone.
The relationship between seed and shoot dry matter increase (R S/W) during the seed-filling period (SFP) can reflect the balance between the assimilate supply and the sink capacity of harvest organs. In a 2-yr experiment, the RS/W in determinate soybean [Glycine max (L.) Merr.] during SFP was investigated under various growing conditions induced by shading and thinning for plants grown under both standard and reduced densities. The RS/W approximated a positive linear regression in both experiments. Slopes of the lines were less than 0.5 and did not vary within the same year even if plant density was reduced. The shoot dry matter increase (W) hence seemed to directly determine seed dry matter increase (S). A positive relationship between total pod number and W or S was observed in both years. Only minimal changes in seed numbers per pod and individual seed weight were observed, regardless of the treatment applied. A positive relationship also existed between total pod number and the number of branch nodes, and hence W increased S via increase in the number of branch node bearing pods. Half the current assimilate product during SFP appeared to be used for increase of vegetative plant parts. This determined total pod number and hence potential S. Determinate soybean is thus very different from cereal crops, In which almost all the current assimilate during the SFP is appropriated to S. The stability of the harvest index in soybean reflects the important effect of the W on pod establishment during SFP.
Springs emitting carbon dioxide are frequent in Central Italy and provide a way of testing the response of plants to CO2 enrichment under natural conditions. Results of a CO2 enrichment experiment on soybean at a CO2 spring (Solfatara) are presented. The experimental site is characterized by significant anomalies in atmospheric CO2 concentration produced by a large number of vents emitting almost pure CO2 (93%) plus small amounts of hydrogen sulphide, methane, nitrogen and oxygen. Within the gas vent area, plants were grown at three sub-areas whose mean CO2 concentrations during daytime were 350,652 and 2370 μmol mol-1, respectively. Weekly harvests were made to measure biomass growth, leaf area and ontogenetic development. Biomass growth rate and seed yield were enhanced by elevated CO2. In particular, onto-morphogenetic development was affected by elevated CO2 with high levels of CO2 increasing the total number of main stem leaf nodes and the area of the main stem trifoliolate leaves. Biochemical analysis of plant tissue suggested that there was no effect of the small amounts of H2S on the response to CO2 enrichment. Non-protein sulphydryl compounds did not accumulate in leaf tissues and the overall capacity of leaf extracts to oxidize exogenously added NADH was not decreased. The limitations and advantages of experimenting with crop plants at elevated CO2 in the open and in the proximity of carbon dioxide springs are discussed.
An experiment was conducted at Lexington, K.Y., USA (38° N latitude) utilizing 18 soybean (Glycine max [L.] Merr.) genotypes in three stem termination groups (determinate, indeterminate, and segregating) to compare eight methods of estimating seed filling period in terms of 1) ability to differentiate among genotypes, 2) lack of bias towards any stem termination type, 3) ease of measurement, and 4) consistency across environments. Growth stage estimates R1 to R8, R4 to R7, R5 to R7 and the reproductive period duration (estimate of the time period of the linear increase in harvest index) detected significant differences among the seed tilling periods of the genotypes. Significant stem termination group measurement method interactions were present, but because the indeterminate stem termination group had a shorter seed filling period than the other two groups for most methods, we were unable to evaluate bias towards stem termination type. The growth stage methods required 4x to 10x less time for data collection than the effective tilling period and reproductive period duration methods. Only the R4 to R7 and R5 to R7 growth stage estimates had significant rank correlation coefficients among genotypes across years. From this and previously published evidence we recommend using the R5 to R7 estimate of seed filling period following the separation of genotypes into stem termination classes for the selection of soybean genotypes based on seed filling period duration.
The linearity of harvest index (HI) increase has provided a simple means to analyze and predict seed growth and yield in experimental and simulation studies. When using the linear HI concept, it is necessary to estimate three parameters: (1) the period from anthesis to the onset of linear increase in HI (LAG), (2) the time of cessation of linear increase in HI (HIM), and (3) the rate of linear increase in HI with time (dHI/dt). Thus, an appropriate method is needed for accurate estimation of the parameters. In this paper, we compare the capability of two methods for this purpose. In the first, conventional method (M1), a simple, linear regression model (y=a+bx) is used to describe HI increase versus time using data contained within the main seed growth period. dHI/dt is taken as b and LAG as −a/b. In this method, physiological maturity is considered as HIM. The second method (M2) is based on applying a non-linear, segmented regression model. The segmented model consists of two intersecting lines, a sloping line (y=a+bx) for the linear increase in HI (b=dHI/dt) and LAG (−a/b) and a horizontal line (y=a+bx0) which determines HIM (x0) and consequently maximum HI (HImax). Data sets of HI versus time for 13 spring wheat (Triticum aestivum L.) cultivars grown under rainfed conditions were used. The segmented model described HI increase with time well and its performance was comparable to quadratic, cubic and logistic models that have been used for this purpose. R2 values for the segmented model were between 0.93 and 0.98. M1 and M2 provided considerably different estimates of LAG, HIM and dHI/dt. Comparison of measured maximum HI with those calculated using parameter estimates of the conventional method (M1) showed that this method is not accurate (RMSE=5.4%). The deficiency in M1 in estimating HI-related parameters may be a source of error in grain yield prediction based on the linear HI increase concept. M2, however, was significantly better (RMSE=0.3%) and thus is proposed as the preferred method for future use.
Several crop simulation models calculate grain yield by assuming that the rate of change of harvest index (δHI/δt) is constant (at rate k) during grain growth. Such behaviour has been identified in many crops, although the literature contains many examples of variations in k. The concept is useful if it approximates the truth in most circumstances, or if departures from both linearity and constancy are predictable from either the environment or the state of the crop. In this paper we examine the hypothesis that much of the variation in k is related to both crop biomass at the start of grain filling (BGF) and the crop growth rate during grain growth (CGF). Calculations using simple partitioning rules indicated that both factors are important. We showed that k increases rapidly as BGF decreases below about 9.0MgDM/ha, but decreases only slowly with increases of BGF above 9.0MgDM/ha. The analysis also showed that the increase in HI with time is quadratic rather than linear. We analysed data from 68 field grown wheat crops with variation in cultivar, location, irrigation, ambient CO2 concentration and sowing dates. These showed an almost three-fold variation in k (0.0058–0.0164day−1). Across all data sets, there was a negative linear relationship (y=0.02−0.0006x, R2=0.41, p
in seed number or seed mass have been somewhat un- successful due to the compensation that occurs between Soybean (Glycine max (L.) Merr.) area has increased tremendously these components (Swank et al., 1987). The compensa- in the upper Midwest over the last decade, but little information tion that occurs between seed mass and number led exists regarding the impact of management systems on soybean yield components. Our objective was to assess the effect of management Hanson (1986) to conclude that soybean seeds are re- system and planting date on soybean seed yield components and their ceptacles for assimilate and that yield-limiting factors development for environments typical of the upper Midwest. A field occur somewhere outside the seed. study was conducted from 1997 to 2000 using five management sys- Yield decreases resulting from drought stress depend tems. Two newer released cultivars (CX232 and Spansoy 250) and both on the phenological timing of the stress and on one older cultivar (Hardin) were planted at two planting dates. Few the degree of yield component compensation. Schou interactions were observed in this study. Management system influ- et al. (1978) reported that yield is more influenced by enced development of the different yield components and produced changes from flowering to physiological maturity com- seed mass ranging from 10.5 to 16.5 g 100 seed 1 , seed number from pared with the emergence to flowering period. Numer-
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