# GrasProg: Pasture Model for Predicting Daily Pasture Growth in Intensive Grassland Production Systems in Northwest Europe

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

^{−1}and were cut on a four-week interval. Average annual dry matter (DM) yields ranged from 10.9 to 11.6 t/ha for the three different locations. The DM accumulation simulated by GrasProg matched actual measurements over the varying intervals well (R

^{2}= 0.65; RMSE = 49.5 g DM m

^{−2}; and NSE = 0.44). Two model parameters were adjusted within the vegetation period, namely, the relative growth rate, a proxy of the number of generative tillers, and the initial biomass at the start of each growth period, a proxy for the tillering density. Both predicted and measured pasture growth rates showed the same typical seasonal pattern, with high growth rates in spring, followed by decreasing growth rates to the end of the vegetation period. These good calibration statistics, with adjusting of only two model parameters, for the different sites and different climatic conditions mean that GrasProg can be used to identify optimum grazing or cutting strategies, with optimal yield and forage quality.

## 1. Introduction

^{−1}day

^{−1}) and, thus, the fodder supply are characterized by maximum rates in spring during the reproductive development of the grass tillers, followed by a subsequent morpho-physiological and drought-related summer depression [16,17]. In the subsequent vegetative stage, after a short recovering due to a second seasonal peak in tillering [18], growth rates decrease steadily until the end of the vegetation period, with large inter- and intra-annual fluctuations depending on temperature, and water and nutrient availability [17]. These phenological and weather-related DM growth rates are a key challenge in pasture management, as a high feed-use efficiency can only be achieved when the supply is synchronized with the feed demand of the animals [19]. Under optimal grazing, the forage utilization efficiency is around 80% [20], while under cut-and-carry systems losses are around 30% due to harvesting, ensiling, and feeding processes [21].

## 2. Materials and Methods

#### 2.1. Study Location

#### 2.2. Experimental Sites

_{t}; kg ha

^{−1}) of four-week-old swards were determined non-destructively using a rising plate meter (Filips Manual Folding Platemeter, Jenquip Agriworks Ltd., Feilding, New Zealand) with five measurements per plot, and using a formula derived for perennial ryegrass-dominated grassland by Trott, Ingwersen [36]:

_{t}) was then calculated as the moving average of four consecutive measurements following Corrall and Fenlon [35]:

_{2}O ha

^{−1}, 53 kg P

_{2}O

_{5}ha

^{−1}, and 30 kg S ha

^{−1}to ensure ideal soil-fertility conditions [37,38]. N fertilizer application was divided into eight equal applications throughout the growing season. For each cut mineral N was applied at a rate of 35 kg N ha

^{−1}(CAN; 13.5% nitrate and 13.5% ammonia N), which resulted in an annual application rate of 280 kg N ha

^{−1}year

^{−1}, based on the recommended rate for intensively managed pastures, according to the rules of good agricultural practice for grazed pastures in Germany (140 kg N ha

^{−1}year

^{−1}), and taking into account a potential N return from excrements of grazing animals [37]. Alternatively, this level of N fertilization (280 kg/ha) is also in line with the recommendations for intensively managed cut-and-carry systems and, thus, representative for the time being.

#### 2.3. Model Description

_{t}(kg DM ha

^{−1}day

^{−1}) from the product of the existing biomass of the previous day (W

_{t−1}), the relative growth rate (RS

_{t}, kg kg

^{−1}day

^{−1}), and an environmental index (GI):

_{t−1}(W

_{0}) in the model describes the existing biomass at the beginning of each growth period, and depends on yield–physiological factors, such as the photosynthetically active residual leaf area and the tiller density at the beginning of the growth period. The initial value of the relative growth rate (RS) during each growth period reflects the phenological development during the upcoming growth period of the pasture, and is related to the proportion of reproductive tillers and, thus, stem elongation and enhanced radiation use efficiency during the growth period [16]. As such, both RS and W

_{0}are time-specific parameters, reflecting the potential productivity of a grassland sward, which is affected by the management intensity (grazing or cutting frequency) and the phenological development under non-limiting weather constraints.

_{t}), which describes the effect of plant ageing as a function of the leaf-area index (LAI), whereby the AGE index decreases with increasing LAI:

_{t}(m

^{2}m

^{−2}) is calculated from W

_{t}using two constants (b = 4.8, c = 0.008), LAI

_{50}= 3 m

^{2}m

^{−2}) is half the assumed maximum LAI, and a = 5.75 is a constant describing the curvature of the function. The beginning of the growth period (AGE = 1) is assumed to be reached when the temperature sum of mean daily temperatures above a base temperature of 0 °C reaches 250 °C.

_{t}) is calculated from the incident global radiation (R; MJ m

^{−2}d

^{−1}) using a saturation function, and increasing with increasing radiation until the insolation at light saturation of the canopy (R

_{opt}) is reached:

_{r}= 2 is a constant describing the curvature of the radiation-response curve.

_{opt}depends on the development of the grassland canopy:

^{2}m

^{−2}(LAI

_{low}), R

_{opt}is low (R

_{low}), and at LAI > 2.5 m

^{2}m

^{−2}(LAI

_{high}) R

_{opt}is high. Values of 22 to 32 MJ m

^{−2}day

^{−1}are used for R

_{low}and R

_{high}, and d

_{r}= 3 is a constant.

_{min}= 1 the minimum temperature below which no growth occurs, and (T

_{opt}= 17) the optimum temperature for maximum growth, T

_{max}= 42, and the constant c

_{t}= 2.

_{a}) to potential evapotranspiration (ET

_{p}), and a soil water index SWI:

_{p}is calculated as a function of the LAI to represent the low evaporative capacity of the grassland canopy:

_{0}, is the reference evaporation rate which is calculated following FAO56 (1999).

_{t−1}is the soil water content at any time, PAW (mm) is the plant-available water in the effective rooting zone, which is defined by the difference between the soil water content at field capacity (SW

_{FC}(mm); −10 kPa matric potential) and permanent wilting point (SW

_{PWP}; −1500 kPa).

_{t−1}is set to PAW at the beginning of the growth period. If precipitation is higher than the maximum daily storage capacity, any exceeding water is directly accounted for as drainage.

^{2}/day), mean daily temperature (°C), precipitation (mm/day), and evaporation (potential evaporation, mm/day), as well as PAW.

#### 2.4. Model Calibration and Statistical Analysis

_{0}and RS were iteratively adjusted in order to minimize the mean squared residuals of measured and simulated biomass, using the R environment (Package nls “nonlinear least squares”; [45]). A mixed linear model was assumed with cut date (week), location, and year as fixed factors and block as a random factor. After a graphical residuals analysis, variance heterogeneity was found for the data. A multiple sample t-test (ANOVA) was performed to test for significant differences in the target variable between factors. Mean comparisons were performed post-hoc with multiple contrast tests. Physiologically reasonable ranges of W

_{t−1}(10–500 kg DM ha

^{−2}) and RS (0.1–0.9 kg kg

^{−1}DM) were used.

^{2}), Nash–Sutcliffe Efficiency (NSE) and root mean square error (RMSE). Since the aim was to parameterize the model for broader use, rather than optimizing for site-specific use, location was considered as a random factor.

## 3. Results

#### 3.1. Growth Rates and Annual Dry-Matter Production

^{−1}for Marshland, 11.2 t DM ha

^{−1}for Geest, and 11.6 t DM ha

^{−1}for Eastern Hills.

#### 3.2. GrasProg Calibration

_{0}.

^{2}of 0.8.

_{0}), a fourth-degree polynomial function as a function of week over the vegetation period was fitted to the measured biomass data (Figure 5):

^{2}of 0.86.

_{0}is close to zero, and the function is characterized by two maximums, one around calendar week 18 and the other one at week 44, close to the end of vegetation.

^{2}of 0.65 and RMSE of 49.5 g DM m

^{−2}and an NSE of 0.64.

## 4. Discussion and Conclusions

^{−1}, as the PAW of the soil had negligible effects. Sufficient precipitation and above-average temperatures in spring and autumn, especially in 2017, led to an extension of the growing season. However, the generative growth of the grass sward in spring is the key factor for the high annual yield levels [47]. Under non-limiting growth conditions such high yields of perennial ryegrass are common, for example, Cashman et al. [48] observed average annual yields of 11.7 t DM ha

^{−1}under similar experimental conditions in Ireland under simulated grazing with 10 cuts with annual N fertilization of 350 kg N ha

^{−1}. High inter-annual yield variations, due to precipitation and temperature [49,50], however, need to be accounted for in the planning of feeding strategies.

_{0}, a proxy for the tillering density, reaches a minimum in mid-June, reflecting tiller death following defoliation of reproductive tillers in the previous cut [18,51], which results in a low production of new tillers. Following this lack of tillers in midsummer a recovery in tillering of all vegetative mother tillers until August is well documented and, thus, W

_{0}increases. The increase in W

_{0}can be additionally explained by the intensive management with 7–8 cuts per year, which enables high light penetration, and, thereby, promotes tillering [52].

^{−1}. Additional N input from grazing animals via excreta and urine ranges from 80 to 95% of the ingested N, depending on animal type, production level, and nitrogen (N) concentration of the herbage [54]. Potential N mineralization rates of these sites with narrow C:N are, thus, likely very high. This, with the good supply of basic nutrients (phosphors, potassium, and sulphur) mean that Schleswig-Holstein is a favorable region for intensively managed grassland due to the advantageous climate and site characteristics.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Dierschke, H.; Briemle, G. Kulturgrasland–Ökosysteme Mitteleuropas aus Geobotanischer Sicht; Eugen Ulmer: Stuttgart, Germany, 2008. [Google Scholar]
- van den Pol-van Dasselaar, A.; Hennessy, D.; Isselstein, J. Grazing of Dairy Cows in Europe—An In-Depth Analysis Based on the Perception of Grassland Experts. Sustainability
**2020**, 12, 1098. [Google Scholar] [CrossRef] [Green Version] - Taube, F.; Gierus, M.; Hermann, A.; Loges, R.; Schonbach, P. Grassland and globalization–challenges for north-west European grass and forage research. Grass Forage Sci.
**2014**, 69, 2–16. [Google Scholar] [CrossRef] - Vogeler, C.S.; Hornung, J.; Bandelow, N.C. Farm animal welfare policymaking in the European Parliament—A social identity perspective on voting behaviour. J. Environ. Policy Plan.
**2020**, 22, 518–530. [Google Scholar] [CrossRef] - Clay, N.; Garnett, T.; Lorimer, J. Dairy intensification: Drivers, impacts and alternatives. Ambio
**2020**, 49, 35–48. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Schils, R.; Philipsen, B.; Hoekstra, N.; Holshof, G.; Zom, R.; Hoving, I.; van Reenen, K.; Stienezen, M.; Klootwijk, C.; van der Werf, J.; et al. Amazing Grazing: A Public and Private Partnership to Stimulate Grazing Practices in Intensive Dairy Systems. Sustainability
**2019**, 11, 5868. [Google Scholar] [CrossRef] [Green Version] - Eurostat. LUCAS the EU’s Land Use and Land Cover Survey. Available online: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=LUCAS_-_Land_use_and_land_cover_survey (accessed on 15 April 2022).
- Destatis. Bodennutzung der Betriebe-Landwirtschaftlich Genutzte Flächen-Fachserie 3 Reihe 3.1.2–2019. 2019. Available online: https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Landwirtschaft-Forstwirtschaft-Fischerei/Publikationen/Bodennutzung/landwirtschaftliche-nutzflaeche-2030312197004.html (accessed on 15 April 2022).
- Smit, H.J.; Metzger, M.J.; Ewert, F. Spatial distribution of grassland productivity and land use in Europe. Agric. Syst.
**2008**, 98, 208–219. [Google Scholar] [CrossRef] - Dellar, M.; Topp, C.; Banos, G.; Wall, E. A meta-analysis on the effects of climate change on the yield and quality of European pastures. Agric. Ecosyst. Environ.
**2018**, 265, 413–420. [Google Scholar] [CrossRef] - Wilkinson, J.M.; Lee, M.R.F.; Rivero, M.J.; Chamberlain, A.T. Some challenges and opportunities for grazing dairy cows on temperate pastures. Grass Forage Sci.
**2019**, 75, 1–17. [Google Scholar] [CrossRef] - White, S.L.; Benson, G.; Washburn, S.; Green, J. Milk Production and Economic Measures in Confinement or Pasture Systems Using Seasonally Calved Holstein and Jersey Cows. J. Dairy Sci.
**2002**, 85, 95–104. [Google Scholar] [CrossRef] - Loges, R.; Mues, S.; Kluß, C.; Reinsch, T.; Lorenz, H.; Humphreys, J.; Taube, F. Eco-efficient milk production in northern Germany inspired by the Irish rotational grazing system. In Sustainable Meat and Milk Production from Grasslands, Proceedings of the 27th General Meeting of the European Grassland Federation, Cork, Ireland, 17–21 June 2018; Horan, B., Hennessy, D., O’Donovan, M., O’Donovan, O., Kennedy, E., McCarthy, B., Finn, J.A., O’Brien, B., Eds.; Wageningen Academic Publishers: Wageningen, The Netherlands, 2018. [Google Scholar]
- Reinsch, T.; Loza, C.; Malisch, C.S.; Vogeler, I.; Kluß, C.; Loges, R.; Taube, F. Proceedings of the New Zealand Society of Animal Production toward Specialized or Integrated Systems in Northwest Europe: On-Farm Eco-Efficiency of Dairy Farming in Germany. Front. Sustain. Food Syst.
**2021**, 5, 167. [Google Scholar] - Dillon, P. Achieving high dry-matter intake from pasture with grazing dairy cows. Frontis
**2007**, 1–26. [Google Scholar] - Taube, F. Growth Characteristics of Contrasting Varieties of Perennial Ryegrass (Lolium perenne L.). J. Agron. Crop Sci.
**1990**, 165, 159–170. [Google Scholar] [CrossRef] - Wingler, A.; Hennessy, D. Limitation of grassland productivity by low temperature and seasonality of growth. Front. Plant Sci.
**2016**, 7, 1130. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Jewiss, O.R. Tillering in grasses—Its significance and control. Grass Forage Sci.
**1972**, 27, 65–82. [Google Scholar] [CrossRef] - Peyraud, J.; Delagarde, R. Managing variations in dairy cow nutrient supply under grazing. Animal
**2013**, 7 (Suppl. S1), 57–67. [Google Scholar] [CrossRef] [Green Version] - Vibart, R.; Vogeler, I.; Dennis, S.; Kaye-Blake, W.; Monaghan, R.; Burggraaf, V.; Beautrais, J.; Mackay, A. A regional assessment of the cost and effectiveness of mitigation measures for reducing nutrient losses to water and greenhouse gas emissions to air from pastoral farms. J. Environ. Manag.
**2015**, 156, 276–289. [Google Scholar] [CrossRef] [PubMed] - Köhler, B.; Thurner, S.; Diepolder, M.; Spiekers, H. Effiziente Futterwirtschaft und Eiweißbereitstellung in Futterbaubetrieben. In LfL-Schriftenreihe; Bayerische Landesanstalt für Landwirtschaft (LfL): Freising, Germany, 2014; ISSN 1611-4159. [Google Scholar]
- Fulkerson, W.J.; Donaghy, D.J. Plant-soluble carbohydrate reserves and senescence-Key criteria for developing an effective grazing management system for ryegrass-based pastures: A review. Aust. J. Exp. Agric.
**2001**, 41, 261. [Google Scholar] [CrossRef] - Barrett, P.D.; Laidlaw, A.S.; Mayne, C.S. An evaluation of selected perennial ryegrass growth models for development and integration into a pasture management decision support system. J. Agric. Sci.
**2004**, 142, 327–334. [Google Scholar] [CrossRef] - Thornley, J.H.M. Grassland Dynamics: An Ecosystem Simulation Model; CAB International: Wallingford, UK, 1998. [Google Scholar]
- Schapendonk, A.; Stol, W.; van Kraalingen, D.; Bouman, B. LINGRA, a sink/source model to simulate grassland productivity in Europe. Eur. J. Agron.
**1998**, 9, 87–100. [Google Scholar] [CrossRef] - Duru, M.; Adam, M.; Cruz, P.; Martin, G.; Ansquer, P.; Ducourtieux, C.; Jouany, C.; Theau, J.; Viegas, J. Modelling above-ground herbage mass for a wide range of grassland community types. Ecol. Model.
**2009**, 220, 209–225. [Google Scholar] [CrossRef] - Brereton, A.J.; O’Riordan, E. A comparison of grass growth models. In Agrometeorological Modelling: Principles, Data and Applications; AGMET: Dublin, Ireland, 2001; pp. 136–155. [Google Scholar]
- Barrett, P.D.; Laidlaw, A.S.; Mayne, C.S. GrazeGro: A European herbage growth model to predict pasture production in perennial ryegrass swards for decision support. Eur. J. Agron.
**2005**, 23, 37–56. [Google Scholar] [CrossRef] - Ruelle, E.; Hennessy, D.; Delaby, L. Development of the Moorepark St Gilles grass growth model (MoSt GG model): A predictive model for grass growth for pasture based systems. Eur. J. Agron.
**2018**, 99, 80–91. [Google Scholar] [CrossRef] - Hanrahan, L.; Geoghegan, A.; O’Donovan, M.; Griffith, V.; Ruelle, E.; Wallace, M.; Shalloo, L. PastureBase Ireland: A grassland decision support system and national database. Comput. Electron. Agric.
**2017**, 136, 193–201. [Google Scholar] [CrossRef] - Petersen-Fredrich, E.C.; Kornher, A.; Taube, F. Ertragsbildung unterschiedlicher Sortentypen des Deutschen Weidelgrases im Vegetationsablauf in Abhaengigkeit vom Nutyungsregime. 2. Mitteilung: Modellberechnungen. Das Wirtsch. Futter
**1989**, 35, 289–300. [Google Scholar] - Herrmann, A.; Kelm, M.; Kornher, A.; Taube, F. Performance of grassland under different cutting regimes as affected by sward composition, nitrogen input, soil conditions and weather—A simulation study. Eur. J. Agron.
**2005**, 22, 141–158. [Google Scholar] [CrossRef] - Hartwich, R.; Haase, G.; Richter, A.; Roeschmann, G.; Schmidt, R. Bodenübersichtskarte von Deutschland 1:1.000.000. 1998. Available online: https://www.bgr.bund.de/DE/Themen/Boden/Produkte/Karten/Downloads/BUEK1000.pdf?__blob=publicationFile&v=2 (accessed on 8 May 2022).
- Ernst, P.; Loeper, E.G. Temperaturentwicklung und Vegetationsbeginn auf dem Grünland. Aus der Grünlandlehranstalt und Marschversuchsstation für Niedersachsen. Das Wirtsch. Futter
**1976**, 22, 5–11. [Google Scholar] - Corrall, A.J.; Fenlon, J.S. A comparative method for describing the seasonal distribution of production from grasses. J. Agric. Sci.
**1978**, 91, 61–67. [Google Scholar] [CrossRef] - Trott, H.; Ingwersen, B.; Wachendorf, M.; Taube, F. Estimation of dry matter yield on permanent grassland by means of height assessment. Pflanzenbauwissenschaften
**2002**, 6, 78–83. [Google Scholar] - DLG. Düngung von Wiesen. DLG-Merkblatt 433. 2018. Available online: https://www.dlg.org/de/landwirtschaft/themen/pflanzenbau/pflanzenernaehrung/dlg-merkblatt-433-duengung-von-wiesen-weiden-und-feldfutter?msclkid=50888a50cf8b11ec88df3ba18bba181b (accessed on 7 May 2022).
- Whitehead, D.C. Nutrient Elements in Grassland: Soil-Plant-Animal Relationships; CABI Publishing: Wallingford, UK, 2000. [Google Scholar]
- Torssell, B.W.R.; Kornher, A.; Svensson, A. Optimization of Parameters in a Yield Prediction Model for Temporary Grasslands; Swedish University of Agricultural Sciences: Uppsala, Sweden, 1982. [Google Scholar]
- Kornher, A.; Torssell, B.W.R. Estimation of parameters in a yield prediction model for temporary grasslands using regional experimental data. Swed. J. Agric. Res.
**1983**, 13, 137–144. [Google Scholar] - Kornher, A.; Torssell, B.W.R. Simulation of weather x management interactions in temporary grasslands in Sweden. Swed. J. Agric. Res.
**1983**, 13, 145–155. [Google Scholar] - Kornher, A.; Torssell, B.W.R. Validation of a yield prediction model for temporary grasslands. Swed. J. Agric. Res.
**1983**, 13, 125–136. [Google Scholar] - Herrmann, A.; Kornher, A.; Ernst, P.; Löpmeier, F.-J.; Taube, F. Reifeprüfung Grünland–Einführung des Prognosemodells in Nordrhein-Westfalen. In 46. Jahrestagung vom 29. bis 31. August 2002 in Rostock, Referate und Poster; Im Auftrag der Arbeitsgemeinschaft für Grünland und Futterbau in der Gesellschaft für Pflanzenbauwissenschaften: Rostock, Germany, 2002; pp. 222–225. [Google Scholar]
- Taube, F.; Kornher, A.; Petersen-Fredrich, E.C. Ertragsbildung unterschiedlicher Sortentypen des Deutschen Weidelgrases im Vegetationsablauf in Abhaengigkeit vom Nutyungsregime. 1. Mitteilung: Ergebnisse eines Feldversuches. Das Wirtsch. Futter
**1989**, 35, 278–288. [Google Scholar] - R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2020; Available online: https://www.R-project.org/ (accessed on 11 April 2022).
- Moriasi, D.N.; Arnold, J.G.; van Liew, M.W.; Bingner, R.L.; Harmel, R.D.; Veith, T.L. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE
**2007**, 50, 885–900. [Google Scholar] [CrossRef] - Parsons, A.J.; Chapman, D.F. The principles of pasture growth and utilization. In Grass: Its Production and Utilization; Hopkins, A., Ed.; Blackwell Science: Oxford, UK, 2000; pp. 31–89. [Google Scholar]
- Cashman, P.A.; McEvoy, M.; Gilliland, T.J.; O’Donovan, M. A comparison between cutting and animal grazing for dry-matter yield, quality and tiller density of perennial ryegrass cultivars. Grass Forage Sci.
**2016**, 71, 112–122. [Google Scholar] [CrossRef] - Enriquez-Hidalgo, D.; Gilliland, T.J.; Hennessy, D. Herbage and nitrogen yields, fixation and transfer by white clover to companion grasses in grazed swards under different rates of nitrogen fertilization. Grass Forage Sci.
**2016**, 71, 559–574. [Google Scholar] [CrossRef] - Vogeler, I.; Thomas, S.; van der Weerden, T. Effect of irrigation management on pasture yield and nitrogen losses. Agric. Water Manag.
**2019**, 216, 60–69. [Google Scholar] [CrossRef] - Korte, C.J. Tillering in ‘Grasslands Nui’ perennial ryegrass swards 2. Seasonal pattern of tillering and age of flowering tillers with two mowing frequencies. N. Z. J. Agric. Res.
**1986**, 29, 629–638. [Google Scholar] [CrossRef] - Grant, S.A.; Barthram, G.T.; Torvell, L. Components of regrowth in grazed and cut Lolium perenne swards. Grass Forage Sci.
**1981**, 36, 155–168. [Google Scholar] [CrossRef] - Whitehead, D.C. The Role of Nitrogen in Grassland Productivity. A Review of Information from Temperate Regions; Commonwealth Agricultural Bureaux: Farnham Royal, UK, 1970. [Google Scholar]
- Deenen, P.J.A.G.; Middelkoop, N. Effects of cattle dung and urine on nitrogen uptake and yield of perennial ryegrass. Neth. J. Agric. Sci.
**1992**, 40, 469–482. [Google Scholar] [CrossRef] - Rueda-Ayala, V.P.; Peña, J.M.; Höglind, M.; Bengochea-Guevara, J.M.; Andújar, D. Comparing UAV-based technologies and RGB-D reconstruction methods for plant height and biomass monitoring on grass ley. Sensors
**2019**, 19, 535. [Google Scholar] [CrossRef] [Green Version] - Cosgrove, G.P.; Betteridge, K.; Thomas, V.J.; Corson, D.C. A sampling strategy for estimating dairy pasture quality. In Proceedings of the New Zealand Society of Animal Production; New Zealand Society of Animal Production: Online, 1998; Volume 58, pp. 25–28. [Google Scholar]

**Figure 1.**Study site locations in Schleswig-Holstein, with locations 1 = Marshland, 2 = Geest and 3 = Eastern Hills.

**Figure 2.**Average monthly temperatures (

**left**) and global radiation (

**right**) for 2016 and 2017, and the long-term mean (1981–2010) from meteorological stations near the study sites.

**Figure 3.**Monthly rainfall for 2016 and 2017, and the long-term mean (1981–2010) from the three meteorological stations near the study sites (

**a**) Marshland, (

**b**) Geest, and (

**c**) Eastern Hills.

**Figure 4.**Daily growth rates for the three main landscape types of Schleswig-Holstein (Marshland, Geest, and Eastern Hills) for two years (2016 and 2017) based on weekly measurements of dry-matter yields using a rising plate meter and calculated as the moving average of four consecutive measurements following Corrall and Fenlon [35].

**Figure 5.**Measured (dots) and fitted values for the biomass at the start of each growth period (W

_{0}; g m

^{−2}) and the relative growth rate (RS; kg kg

^{−1}day

^{−1}) over the vegetation period, using a fourth-degree polynomial function as a function of the week of the year for W

_{0}and an exponential function for RS.

**Figure 6.**Measured vs. predicted pasture biomass of different cuts for two years (2016 and 2017) and for the three main landscape types of Schleswig-Holstein (Marshland, Geest, and Eastern Hills) based on weekly measurements of dry-matter yields using a rising plate meter and calculated as the moving average of four consecutive measurements following Corrall and Fenlon [35].

**Table 1.**Site Characteristics of the study sites, with soil classification based on the FAO system [33], with PAW (plant-available water) and C:N provided for the top 300 mm.

Site | Landscape | Soil Classification FAO | Soil Type | Texture (%) Clay/Silt/Sand | PAW (mm) | C:N |
---|---|---|---|---|---|---|

1 | Marshland | Kleimarsch (Eutric Fluvisols) | clayey loam | 30/50/20 | 84 | 9 |

2 | Geest | Podsol-Gley/Gley-Podsol; Gley-Treposol | sandy sand | 5/9/86 | 42 | 13 |

3 | Eastern Hills | Parabraunerde (Haplic Luvisols) | loamy sand | 15/24/61 | 80 | 10 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Peters, T.; Kluß, C.; Vogeler, I.; Loges, R.; Fenger, F.; Taube, F.
GrasProg: Pasture Model for Predicting Daily Pasture Growth in Intensive Grassland Production Systems in Northwest Europe. *Agronomy* **2022**, *12*, 1667.
https://doi.org/10.3390/agronomy12071667

**AMA Style**

Peters T, Kluß C, Vogeler I, Loges R, Fenger F, Taube F.
GrasProg: Pasture Model for Predicting Daily Pasture Growth in Intensive Grassland Production Systems in Northwest Europe. *Agronomy*. 2022; 12(7):1667.
https://doi.org/10.3390/agronomy12071667

**Chicago/Turabian Style**

Peters, Tammo, Christof Kluß, Iris Vogeler, Ralf Loges, Friederike Fenger, and Friedhelm Taube.
2022. "GrasProg: Pasture Model for Predicting Daily Pasture Growth in Intensive Grassland Production Systems in Northwest Europe" *Agronomy* 12, no. 7: 1667.
https://doi.org/10.3390/agronomy12071667