GrazeGro: a European herbage growth model to predict pasture production in perennial ryegrass swards for decision support

Barrett, P. D.; Laidlaw, A. S.; Mayne, C. S.

doi:10.1016/j.eja.2004.09.006

Cited by 49 publications

(41 citation statements)

References 20 publications

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“…A lot of models were thus proposed in literature ranging from complex approaches simulating growth for different crops such as CERES (Jones et al, 2003), WOFOST (Pogacar and Kajfez-Bogataj, 2009), STICS (Brisson et al, 2003) up to whole farm optimization models such as GRAZEGRO (Barrett et al, 2005), MOD-ERATO (Bergez et al, 2002). Modeling of grassland requires to define growth functions quite different compared with those representing cereal development.…”

Section: Introductionmentioning

confidence: 99%

Combined use of FORMOSAT-2 images with a crop model for biomass and water monitoring of permanent grassland in Mediterranean region

Courault

Hadria²,

Ruget³

et al. 2010

Hydrol. Earth Syst. Sci.

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Abstract. The aim of this study is to propose methods to improve crop and water management in Mediterranean regions. At landscape scale, there is a spatial variability of agricultural practices, particularly for grasslands irrigated by flooding. These grasslands are harvested three times per year and produce high quality hay, but their productions decreased significantly during the last few years because of the water scarcity. It is therefore important to assess the real water requirement for crops in order to predict productions in the case of agricultural practice modifications. Until now, the spatial variability of agricultural practices was obtained through surveys from farmers, but this method was tedious to describe an entire region. Thus, the specific aim of the study is to develop and assess a new method based on a crop model for estimating water balance and crop yield constrained by products derived from optical remote sensing data with high spatio-temporal resolution.A methodology, based on the combined use of FORMOSAT-2 images and the STICS crop model, was developed to estimate production, evapotranspiration and drainage of irrigated grasslands in "the Crau" region in the South Eastern France. Numerous surveys and ground measurements were performed during an experiment conducted in 2006. Simple algorithms were developed to retrieve the dynamic of Leaf Area Index (LAI) for each plot and the main agricultural practices such as mowing and irrigation dates. These variables computed from remote sensing were then used to parameterize STICS, applied at region scale to estimate the spatial variability of water budget associated with the biomass productions. Results are displayed at the farm Correspondence to: D. Courault (courault@avignon.inra.fr) scale. Satisfactory results were obtained when compared to ground measurements. The method for the extrapolation to other regions or crops is discussed as regard to data available.

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Section: Introductionmentioning

confidence: 99%

Combined use of FORMOSAT-2 images with a crop model for biomass and water monitoring of permanent grassland in Mediterranean region

Courault

Hadria²,

Ruget³

et al. 2010

Hydrol. Earth Syst. Sci.

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show abstract

“…The fraction tillers at or beyond the heading stage is calculated from the heading date (Eq. 1, Barrett et al, 2005).…”

Section: Extension and Adaptation Of Lingramentioning

confidence: 99%

“…LINGRA simulates the green aboveground biomass and the dead biomass, but does not distinguish between vegetative and generative growth. Vegetative and generative growth, however, affect feed quality to a large extent (Höglind et al, 2001, Barrett et al, 2005. Since feed quality is an important input for LiGAPS-Beef, we included vegetative and generative growth in LINGRA to simulate grass quality.…”

Section: Extension and Adaptation Of Lingramentioning

confidence: 99%

“…Where GT is the number of generative tillers (m -2 ), T 1st Jan. is the number of tillers present at the 1 st of January, and S(t) is the probability of a tiller to survive up to time 4 Derived from Lambert and Litherland (2000) Decline ME in generative tillers For the equation, see Barrett et al (2005). DM = dry matter; GDD = growing degree days; LUE = light use efficiency; ME = metabolisable energy; PAR = photosynthetic active radiation; SLA = specific leaf area step t from the 1 st of January, which was already included in the original version of LINGRA.…”

Section: Extension and Adaptation Of Lingramentioning

confidence: 99%

“…Each of the tillers present at the 1 st of January is assumed to be sufficiently vernalized to become generative in the next growing season (Barrett et al, 2005). Heading is assumed to occur at a developmental stage of 975 growing degree days (Table 1A.1), which allows to calculate the heading date of the grass.…”

Section: Extension and Adaptation Of Lingramentioning

confidence: 99%

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GrazeGro: a European herbage growth model to predict pasture production in perennial ryegrass swards for decision support

Cited by 49 publications

References 20 publications

Combined use of FORMOSAT-2 images with a crop model for biomass and water monitoring of permanent grassland in Mediterranean region

Combined use of FORMOSAT-2 images with a crop model for biomass and water monitoring of permanent grassland in Mediterranean region

A generic method to analyse yield gaps in feed-crop livestock systems

A Component-Based Framework for Simulating Agricultural Production and Externalities

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