The document describes statistical growth models developed to predict dry matter yield and digestibility of timothy and meadow fescue swards in Finland. It provides 8 models using variables like temperature sum, growing time, location coordinates, and precipitation to estimate values for 3 harvests. The models were developed using data collected from different Finnish regions between 1996-2016. The aim was to improve methods of estimating how grass growth and digestibility decline are affected by climate in Finland, though variability means reliability needs assessment case by case.

1. Models
Models for DM yield:
M3: 1st harvest:
0.7 × 1000 × (-1.20 + 0.021 × ), RMSE = 1207, RPE = 0.29
M5: 2nd harvest:
0.7 × (-1511 -2.621 × + 164.0 × -0.786 × ), RMSE = 1301,
RPE = 0.400
M7: 3rd harvest:
0.7 × (4436.71 -57.858 × + 793.62 × ln( )), RMSE = 501, RPE = 0.161
Models for digestibility (D-value):
M1: 1st harvest (a):
776.27 -216.21 × exp(-exp(-0.0082 × ( -327.75) -0.0165 × ( -3204974) ×
( -6666700) × 10-10 - 0.0276 × ( - 3204974) × 10-5 +0.0261 × (
6666700) × 10-5)), RMSE = 21.6, RPE = 0.024
M2: 1st harvest (b):
769.5 -exp(5.61 × (1 -exp(-0.07 × ( × 10-1 + 0.016 × -7.58)))),
RMSE = 21.8, RPE = 0.024
M4: 2nd harvest:
779.1 -0.3178 × + 0.0002 × RMSE = 34.5, RPE = 0.041
M6: 3rd harvest:
723.70 + 0.6127 × -0.1055 × -7.8189 × P3, RMSE = 16.8, RPE = 0.017
The aim of the study
Statistical growth models were developed for predicting dry matter (DM) yield (kg DM ha-1) and digestibility (D-value; g
kg-1 DM) of timothy (Phleum pratense L.) and meadow fescue (Festuca pratensis Huds.) swards in Finland. Models are
available for all three harvests that are commonly taken in the region.
Grass growth models for
estimating digestibility
and dry matter yield of
forage grasses in Finland
EGF 2018, Cork, Ireland, 17-21 June 2018
Corresponding author: maarit.hyrkas@luke.fi
Hyrkäs, M., Korhonen, P., Pitkänen, T., Rinne, M. and Kaseva, J.
Natural Resources Institute Finland (Luke)
© M. Hyrkäs
Materials and methods
The models are based on data collected from different regions in Finland between 1996 and 2016. The updated models
are regression or random regression models where the explanatory variables are temperature sum or growing time.
Other parameters, such as geographical location and the date of the previous cut, are also used.
Conclusions
• The new combined model improves methods of estimating how grass growth and the decline of digestibility are
affected by temperature and growing time in Finnish climate conditions.
• The reliability of the model’s estimates needs to be assessed case by case, due to the high variation caused by non-
climatic factors.
520
570
620
670
720
770
820
0 200 400 600 800 1000 1200
g kg-1 DM
Effective temperature sum, °C d
D-value
(M1) 1. cut
(M2) 1. cut
(M4) 2. cut
(M6) 3. cut
Location: Maaninka
63°14′ N 27°31′ E
PreGS28 = 78.1 C°
T2 = 79 d (19th July)
0 50 100 150
Growing time, d
(M5) 2. cut
(M7) 3. cut
TS1 = 341 °C d
T2 = 79 d (19th July)
0
1000
2000
3000
4000
5000
6000
7000
8000
0 200 400 600
kg DM
ha-1
Temperature sum, °C d
DM yield
(M3) 1. cut
Variables used in the models.
x
X-coordinate of location in Finnish Uniform Coordinate
System (YKJ)
y
Y-coordinate of location in Finnish Uniform Coordinate
System (YKJ)
TSz, z = 1,2,3 °C d
Effective temperature sum (base temperature 5 °C) during
yield development z
GTz, z = 1,2,3 d Growing time of yield z
T2 d Number of days from 1 May to the second cut
P3 mm Daily mean precipitation during development of the third yield
PreGS28 °C
Sum of the mean temperatures of the 28 days before the
beginning of the growing season