© The Authors, 2024, Published by the Universidad del Zulia*Corresponding author:jorge.pinnacabrejos@gmail.com
Keywords:
Sandy
Aridity
Spacings
Evapotranspiration
Temperature
Yield of sugar beet with drip irrigation, with Penman’s equation and AquaCrop model
Rendimiento de remolacha azucarera bajo riego por goteo con ecuación de Penman y modelo
AquaCrop
Rendimento de beterraba com irrigação por gotejamento, com equação de Penman e modelo
AquaCrop
Jorge Pinna Cabrejos
1*
Kevin Rivas Quevedo
2
Rev. Fac. Agron. (LUZ). 2024, 41(2): e244115
ISSN 2477-9407
DOI: https://doi.org/10.47280/RevFacAgron(LUZ).v41.n2.05
Crop production
Associate editor: Dr. Jorge Vilchez-Perozo
University of Zulia, Faculty of Agronomy
Bolivarian Republic of Venezuela
1
Universidad Privada Antenor Orrego, Facultad de Ciencias
Agrarias, Escuela de Ingeniería Agrónoma, Av. América Sur
3145, Urb. Monserrate, Trujillo, Perú.
2
Egresado
de la Universidad Privada Antenor Orrego,
Facultad de Ciencias Agrarias, Escuela de Ingeniería
Agrónoma, Av. América Sur 3145, Urb. Monserrate, Trujillo,
Perú.
Received: 28-03-2024
Accepted: 15-04-2024
Published: 25-04-2024
Abstract
It is necessary to estimate sugar beet yield, because studies with this
crop demonstrated than in Peruvian coastal zone, could be a protable
crop. The objective of the present experiment was to know if dry matter
yield of sugar beet is related with Penman’s equation, or FAO’s AquaCrop
model. Experiment was made in a sandy soil, non-salty, calcareous, very
poor in organic matter, with drip irrigation in Peruvian northern coast. Four
treatments: two, three, four and ve plant rows per irrigation drip line, in a
completely random design, with four replications were utilized. Calculated
fresh matter weighs with AquaCrop were between 15.5 and 24.5 Mg.ha
-1
,
very much lesser to real ones (between 67.5 and 103.9 Mg.ha
-1
) hence Aqua
Crop model is not eective to estimate yield of sugar beet. It is possible to
estimate yield of sugar beet, with Penman’s formula, which varied between
11.40 and 27.96 Mg.ha
-1
dry weight, and the real one was between 13.4 and
21.5 Mg.ha
-1
, with a “Root Mean Square Error” (RMSE) of 3.73.
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Rev. Fac. Agron. (LUZ). 2024, 41(2): e244115 April-June. ISSN 2477-9407.2-6 |
Resumen
Es necesario estimar el rendimiento de la remolacha azucarera,
ya que los estudios con dicho cultivo han demostrado en la costa del
Perú que puede ser rentable. El objetivo del presente experimento fue
conocer si el rendimiento de materia seca de la remolacha azucarera
se relaciona con la ecuación de Penman o con el modelo AquaCrop
de la FAO. El experimento se efectuó en un suelo arenoso, no salino,
calcáreo, muy pobre en materia orgánica, con riego por goteo, en la
costa norte del Perú. Se establecieron cuatro tratamientos: dos, tres,
cuatro y cinco líneas de plantas por lateral de riego en un diseño de
bloques completos al azar, con cuatro repeticiones. Los pesos frescos
calculados con el modelo AquaCrop variaron entre 15,5 y 24,5
Mg.ha
-1
y son muy inferiores a los reales (entre 67,5 y 103,9 Mg.ha
-1
)
por lo que no es adecuado para estimar el rendimiento de la remolacha
azucarera. Mientras que sí es posible estimar el rendimiento de la
remolacha azucarera con la ecuación de Penman, el que varió entre
11,40 y 27,96 Mg.ha
-1
de peso seco, y el real estuvo entre 13,4 y 21,5
Mg.ha
-1
, con un “Error Cuadrático Medio” (RMSE) de 3,73.
Palabras clave: arenoso, aridez, distanciamientos, evapotranspiración,
temperatura.
Resumo
É necessário estimar o rendimento da beterraba sacarina, já que
os estudos com essa cultura demonstraram na costa do Peru que
pode ser rentável. O objetivo da presente experiência era saber se o
rendimento de matéria seca da beterraba se relaciona com a equação
de Penman ou com o modelo AquaCrop da FAO. A experiência foi
realizada em solo arenoso, não salino, calcário, muito pobre em
matéria orgânica com irrigação por gotejamento, na costa norte do
Peru. Foram estabelecidos quatro tratamentos: dois, três, quatro
e cinco linhas de plantas de irrigação por lateral de Riego em um
projeto de blocos completos aleatórios, com quatro repetições. Os
pesos frescos calculados com o modelo AquaCrop variaram entre
15.5 e 24.5 Mg.ha
-1
e são muito inferiores aos reais (entre 67.5 e
103.9 Mg.ha
-1
) pelo que não é adequado para estimar o rendimento da
beterraba sacarina. Enquanto que é possível estimar o rendimento da
beterraba com a equação de Penman, que variou entre 11.40 e 27.96
Mg.ha
-1
de peso seco, e o real esteve entre 13.4 e 21.5 Mg.ha
-1
, com
um “Erro Quadrático Médio” (RMSE) de 3.73.
Palavras-chave: arenoso, aridez, espaçamentos, evapotranspiração,
temperatura.
Introduction
In the Peruvian coast (Valdivia et al., 2001) developed experiments
with sugar beet (Beta vulgaris L. subsp. vulgaris var. altissima Döll) in
highly saline soils that do not allow another crop, and Reynoso et al.,
(2001) mentioned that in saline soils it can be employed as a protable
crop by producing 90 Mg.ha
-1
of roots. In France it produces a lot of
sugar per hectare/year (around 13.7 Mg.ha
-1
of sugar) (Heno et al.,
2018), and it is one of the most protable crops for ethanol extraction
(Zicari et al., 2019). In Peru, it already produced similar amounts of
sugar (Reynoso et al., 2001). It is required to expand the knowledge
of B. vulgaris L. to move to the next stage, as an industrial crop, so it
is necessary to estimate its yield being precision agriculture the most
appropriate method, and for this purpose it is required that the indices
obtained through satellite methods are validated with eld data. To
validate the yield obtained, the water consumption by the same can be
used with the AquaCrop “model” of FAO (2012), or with the Penman
equation (1971). It is necessary to indicate that the Penman equation
(1971) calculates crop yield with the water consumed, and should not
be confused with the Penman-Monteith formula that calculates crop
evapotranspiration (FAO, 2006) with meteorological data.
The AquaCrop model is eective when working with irrigated
sugar beet, it predicts yield, biomass, water productivity (Araji et al.,
2019), tuberous root yield (Bitri and Grazhdani, 2015), being a good
model in general (Sanchez-Sastre et al., 2020). But it is not so good
when there is water stress due to excess or lack of moisture (Stricevic
et al., 2011; Alishiri et al., 2014; Malik et al., 2017; Garcia-Vila et al.,
2019) or lack of nitrogen (Alishiri et al., 2014).
Crop yield, mathematically expressed with an equation (Penman,
1971), depends on its water utilization (evapotranspiration), if there
are no phytosanitary or water decit problems. This equation, with
some adaptations, has been successfully used in pastures by Fitzgerald
et al. (2005; 2008), and in sugarcane by Pinna et al. (1983), who
indicate that the solar radiation xation eciency (ɛ) varies with the
dierent cultivars in various parts of the world between 1.15 % and
4.14 %, and found a value of 1.75 % for the Peruvian coast in the
cultivar H32-8560. For this last crop and two other cultivars Burgos
(1984) shows values of 1.5 % and 3.9 %; and also 1.9 % for sugar
beet. The eciency (ɛ) varies according to the type of crop; Penman
(1971) indicates a value of 0.9 % for grass and 1.4 % for potato.
Researchers also use a similar, more specic concept, which
is the Solar Radiation Utilization Eciency (RUE) that relates
biomass production to the Photosynthetically Active Radiation (PAR)
intercepted by a crop (Mariscal et al., 2000) expressed in grams of
dry matter per Mega Joule of photosynthetically active radiation (g
DM.MJ PAR
-1
) (Homann and Kenter, 2018) (Homann and Kenter,
2018).
Both concepts were used by Monteith (1977) who showed the
same value for barley, potato, apple, and sugar beet crops: 1.4 g.MJ
-1
equivalent to an eciency (ɛ) of 2.4 %. Homann and Kluge-Severin
(2010) indicated an RUE of 1.2 g.MJ
-1
for sugar beet. RUE varies
with cultivars, with irrigation, with seeding density and whether they
are C3 or C4 plants, in many crops and in dierent countries (Hateld
et al., 2019; Rong et al., 2021); also, with regions (countries), with
nitrogen application, with crop management (Rong et al., 2021) and
with tillage type (Hateld et al., 2019).
In potato, whose RUE is the same as that of sugar beet according
to Monteith (1977), it is higher than in other C3 crops and even
higher than in some C4 crops, being 3.5-3.7 g.MJ
-1
in England, 3.9
in Scotland, 3.2-3.8 in Japan and 2.9-3.0 in Denmark (Lizana et al.,
2021) higher than those indicated by Monteith (1977) for potato
and sugar beet in England. Lizana et al. (2021) report a value of 5.9
for potato in the central coast of Peru and indicate that RUE varies
with genotypes, nitrogen deciency and the presence of nematodes.
The objective of the experiment was to determine whether the dry
matter yield of sugar beet irrigated by drip irrigation is related to the
Penman equation (1971) or to the AquaCrop model of FAO (2012).
Materials and methods
Study site
The work was developed with data from an experiment with drip
irrigation already published (Rivas and Pinna, 2021), at the Fundo
Agroindustrial UPAO, northern coast of Peru (8°12’10.22.22’’ S,
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Pinna and Rivas. Rev. Fac. Agron. (LUZ). 2024 41(2): e2441153-6 |
78°58’10.95’’ W) in a sandy soil. The Peruvian coast is classied as
a hyperarid region (UNESCO, 1977), subtropical desert (Tosi, 1960).
This climate has not varied much over time (SENAMHI, 2020). The
experiment was conducted in winter, with minimum temperatures
higher than those found in other latitudes, in other tropical countries;
and maximum temperatures well below those found in those places
due to the special characteristics of the climate, which lead to almost
zero precipitation (table 1).
Crop management
An initial irrigation was carried out to reach a eld capacity
humidity at a depth of 1 m. A wet “blanket” was obtained at depth
without dierentiated bulbs. Direct sowing was carried out, one seed
per stroke, in a cone 5 cm deep and 3 cm in diameter drilled in the soil,
which was lled with a 1/1 mixture of river sand and worm humus,
where the seed was placed. Cooper cultivar sugar beet monogerm
seed was used. Because sugar beet has a germination percentage of
about 80 % and because germination was not uniform in all the plots,
it was reseeded at 22 and 29 days and transplanted at 46 days.
Irrigation was done from 1.20 hours to 2 hours daily until
50 days after planting (dap), to replenish the moisture lost by
evapotranspiration, and to maintain the moisture at eld capacity, the
rst time based on the formula that calculates the sheet to be applied,
with the eld capacity, moisture content, bulk density, root depth;
later “adjusted” with observations of soil pits. From day 51 onwards,
daily irrigation was started based on the actual Kc (crop coecient,
coverage, measured in the eld, with a tape measure, leaf area, leaf
area, “green”, over total area) multiplied by Eo (tank evaporation),
that is, with the evapotranspiration of the crop, which is equal to Eo
multiplied by Kc. The soil was maintained at eld capacity, since the
water lost by evapotranspiration was replaced daily. An application
eciency of 100 % was considered.
The reference evapotranspiration (ETo) was not used, but the
evapotranspiration calculated with Kc and Eo, since in the experimental
area (and in the entire Peruvian coast) the wind speed is low and the
relative humidity high (Table 1), so this evapotranspiration is a better
indicator than the reference evapotranspiration (FAO, 2006). Actual
(measured) and variable Kc were taken for each treatment. A class “A”
evaporimeter tank was used, which is used to irrigate an area of about
150 ha. At 14 dap, fertilization was started using weekly fertigation
(total dose 150 N - 80 P
2
O
5
- 200 K
2
O) following a template prepared
according to the needs of the crop during the various physiological
stages; and was harvested at 170 dap.
Treatments and data analysis
A randomized complete block design with four treatments and
four replications, was used: T1, two lines of plants located every 11
cm, per lateral (line) of irrigation, (hoses, with drippers every 40 cm,
and an expense (Q) of 1.5 L.h
-1
each); T2, three lines per lateral every
17 cm; T3, four lines every 22 cm; T4, ve lines every 28 cm. Plant
spacings were 0.11 m, 0.17 m, 0.22 m and 0.28 m between plants,
with 2, 3, 4 and 5 lines of plants per lateral, respectively, and 1.80 m
between laterals. Plant density was similar, i.e., around 100,000 plants
per hectare (101,010 plants.ha
-1
between 11 cm, 98,039 between 17
cm, 101,010 between 22 cm and 99,206 between 28 cm). The 100
m long furrow was divided into 4 treatments of 25 m length each.
Having more rows meant more irrigation because the Kc measured
was higher, and higher yields were obtained, which widened the
range of data, improving the comparison of the two methodologies.
Regression analysis was performed as a measure of the “t” of
the relationship between measured and calculated data, by means of
the coecient of determination. The agreement between the models
and the observed data was performed with the “Mean Squared
Error” (RMSE), considering that the lower the value, the better the
agreement, and when the RMSE is normalized, less than 10 % is
excellent, 10 to 20 % good, 20 to 30 % regular, and more than 30 %
bad; and the agreement index “d” which is excellent when it is one (1)
and very bad close to zero (0) (Garcia-Vila et al., 2019).
Yield evaluation
Root dry matter, is 24 % of root fresh weights (Reynoso et al.,
2001; Valdivia et al., 2022), that of leaves plus crowns, is 14 % of
fresh weight (Valdivia et al., 2022). For the total yield, both fresh and
dry matter, brous roots were not considered, because at harvest they
are only 3 % of the total biomass (Vamerali et al., 2009).
The Penman equation (1971):
Y/Et = 39ɛ Mg.ha
-1
.cm
-1
(1)
where Et is the accumulated transpiration in cm, Y is the total dry
matter production in Mg.ha
-1
and (ɛ) is the solar radiation xation
eciency (eciency of conversion of radiation received on the crop
surface, into dry matter -converted into energy units-), we worked
with three eciencies (ɛ) indicated for sugar beet in the literature:
1.9 % (Burgos, 1984), 2.4 % (Monteith, 1977) and 3.77 % (Homann
and Kenter, 2018), in the latter case, with Monteith’s (1977) ratio
Table 1. Meteorological data. Monthly average from June 2 to November 18, 2017.
Month
Maximum
temperature
Minimum
temperature
Mean temperature Relative humidity Rainfall Wind speed
ºC ºC ºC % mm km.h
-1
Jun 22.4 16.6 19.5 84.9 0.00 5.4
Jul 21.7 16.1 18.9 84.8 0.01 5.1
Aug 19.9 15.4 17.6 84.4 0.05 5.1
Sep 19.5 14.7 17.1 89.2 0.04 5.4
Oct 19.8 14.6 17.2 89.4 0.02 4.6
Nov 23.0 16.6 19.8 75.0 0.06 5.8
(Rivas y Pinna, 2021).
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between RUE and (ɛ) (1.4 g.MJ
-1
, is equivalent to an eciency (ɛ) of
2.4 %). Eciency (ɛ) data are not available for Peru.
Data for AquaCrop
For AquaCrop, the data in 1able 1 were used, when available, and
when not, with the default data of the program, which according to
Sanchez-Sastre et al. (2020) are well calibrated, which imply small
changes in the default data and in the estimates of yields (FAO, 2012).
Temperatures were worked by month. All requested variables were
entered into the model, at the required frequencies, and are the same
for all treatments, except Kc and irrigation.
Results and discussion
Table 2 shows the water used and the Kc employed per tens (ten
days) for AquaCrop (water was applied dierentially, daily, for each
treatment according to its Kc in equal amounts to each replicate). As
Kc was measured, in the rst tens of the crop, when the leaf area was
very small, Kc were also extremely low. Root yields are shown in
table 3.
Yields estimated with AquaCrop increase with drip line
when fresh weight as with the real ones, but not in dry weight (table
4) (weight which is important for estimates with Penman (1971) as
with the RUE) since the harvest index (HI) shown by the model as a
result after its execution, for dry weights: 84 %, 83.8 %, 81 %, and
70.2 % for two, three, four, and ve rows, decreases with the number
of rows instead of increasing, or being the same, despite the data
being the same for all treatments except Kc and irrigation. Between
the estimated and measured yield in fresh weight, the RMSE shows
a low agreement, and when normalized, it is very low, the “d” index
shows an extremely low one; and it has a moderately good t since
the regression between the actual fresh weight with the one calculated
with AquaCrop is: Y=0.2363X+1.2773 (R
2
0.7843). The estimated
fresh weights are much lower than the actual ones, by almost four
times, which is in agreement with other crops (Sherzod et al., 2023)
and explains the low agreement.
Table 2. Water applied in the experiment (mm).
Treatments Te I II III IV V VI
Dt 2-11 jun 12-21 jun 22 jun-1 jul 2-11 jul 12-21 jul 22-31 jul
Kc mm.d
-1
Kc mm.d
-1
Kc mm.d
-1
Kc mm.d
-1
Kc mm.d
-1
Kc mm.d
-1
Two rows
0.002 0.04 0.06 2.477 0.16 5.23 0.20 1.55 0.20 4.90 0.34 7.95
Three rows 0.004 0.08 0.07 3.059 0.20 6.72 0.25 5.90 0.39 9.56 0.52 13.46
Four rows 0.005 0.10 0.07 2.848 0.18 6.09 0.23 5.34 0.46 11.28 0.64 15.49
Five rows 0.007 0.15 0.01 0.372 0.27 7.28 0.28 6.43 0.71 17.40 0.88 22.74
Treataments Te VII VIII IX X XI XII
Dt 1-10 aug 11-20 aug 21-30 aug 31 aug-9 sep 10-19 sep 20-29 sep
Kc mm.d
-1
Kc mm.d
-1
Kc mm.d
-1
Kc mm.d
-1
Kc mm.d
-1
Kc mm.d
-1
Two rows
0.34 9.63 0.34 10.54 0.34 9.61 0.34 13.26 0.34 12.58 0.34 8.16
Three rows 0.52 13.59 0.52 16.12 0.52 13.75 0.52 20.28 0.52 19.24 0.52 12.48
Four rows 0.64 16.23 0.64 19.84 0.64 16.51 0.64 24.96 0.64 23.04 0.64 15.36
Five rows 0.88 21.51 0.88 27.28 0.88 22.03 0.88 34.32 0.88 32.56 0.88 21.12
Treatments Te XIII XIV XV XVI XVII Total
Dt 30 sep-9 oct 10-19 oct 20-29 oct 30 oct-8 nov 9-17 nov
Kc mm.d
-1
Kc mm.d
-1
Kc mm.d
-1
Kc mm.d
-1
Kc mm.d
-1
mm
Two rows
0.34 17.75 0.34 11.22 0.34 15.98 0.34 13.78 0.27 9.06 153.79
Three rows 0.52 23.16 0.52 17.16 0.52 24.44 0.52 21.16 0.42 13.86 234.20
Four rows 0.64 26.78 0.64 21.12 0.64 30.08 0.64 25.96 0.51 17.37 278.39
Five rows 0.88 34.00 0.88 29.04 0.88 41.36 0.88 35.80 0.71 23.91 377.31
Te = Tens; Dt = Dates; Kc = crop coecient; mm.d
-1
= millimeters per tens.
Table 3. Average yield of the four treatments.
Treatments Yield Mg.ha
-1
Roots Leaves plus Crowns Total
Fresh Dry Fresh Dry Fresh Dry
Two rows
39.9 9.6 27.6 3.9 67.5 13.4
Three rows 51.0 12.2 29.2 4.1 80.2 16.3
Four rows 58.6 14.1 25.9 3.6 84.4 17.7
Five rows 69.5 16.7 34.5 4.8 103.9 21.5
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Pinna and Rivas. Rev. Fac. Agron. (LUZ). 2024 41(2): e2441155-6 |
Table 4. Estimated yields and their agreement with Penman and AquaCrop methods.
Treatments Yield Mg.ha
-1
Penman AquaCrop
Dry
ɛ(1.90 %)
Dry
ɛ(2.40 %)
Dry
ɛ(3.77 %)
Fresh Dry
Two rows
11.40 14.39 22.61 15.50 13.04
Three rows 17.34 21.90 34.41 21.00 17.60
Four rows 20.63 26.06 40.93 23.50 19.04
Five rows 27.96 35.32 55.48 24.50 17.18
RMSE
3.73 8.57 22.95 63.69 2.38
RMSE Normalized (%)
21.67 49.71 133.19 75.82 13.68
d
0.83 0.54 0.21 0.04 0.74
RMSE = root mean square error; d = concordance index
y = 2.0635x
- 16.227
R² = 0.9987
y = 2.608x
- 20.525
R² = 0.9987
y = 4.0956x - 32.219
R² = 0.9987
y = 1.0995x + 2.1784
R² = 0.8328
y = 0.4815x + 8.4178
R² = 0.3905
10
20
30
40
50
60
10 20 30
Calculated yield Mg.ha
-1
Real yield : Dry yield Mg.ha
-1
Penman 1.9%
Penman 2.4%
Penman 3.77%
Aqua Crop Fresco
Aqua Crop Seco
The dry weights calculated with AquaCrop are similar to the real
ones, have a good agreement according to the RMSE, and according
to the normalized one, good. They have a high “d” index, but with
a very low t because they do not follow the same trend, especially
in the ve-row data (R
2
= 0.3905) (gure 1). The contradiction in
AquaCrop between fresh and dry weights indicates that this model
is not ideal for estimating sugar beet yields, because the crop itself is
very important in the calculations it develops, such as, for example,
the harvest index (HI), which cannot be adjusted or modied, because
it is a result of the model itself. It also depends on the absence of
stress of any kind, whether due to the presence of weeds, pests or
diseases (Pinheiro et al., 2024). When there is water stress in sugar
beet, the results are very variable (Stricevic et al., 2011; Alishiri et
al., 2014; Malik et al., 2017; Garcia-Vila et al., 2019), also due to
lack of nitrogen (Alishiri et al., 2014); in this study, the stress that
occurred was due to nematode attack, which produced deciencies in
the assimilation of water and nutrients (Shakeel et al., 2022).
The coecient of determination (R
2
) between actual dry weight
and calculated fresh yield, surprisingly, is better than dry versus dry
(gure 1) and fresh versus fresh, with AquaCrop, despite the fact that
Figure 1. Regressions between actual dry weights and calculated
yields.
the default data used are the same in dry weight, fresh and treatments,
which reinforces the idea that it is not an adequate model to estimate
beet yield.
With Penman’s (1971) equation, only dry weights are calculated,
which increase with rows per lateral table 4) as well as the real ones
(table 3), since the applied water increased with rows because its Kc
also increased. Except with the ɛ of 1.9 % the calculated yields are
much higher than the actuals. The RMSE between the real data and
that calculated with the ɛ of 1.9 % is good, the normalized one is
regular and the high “d” index, very good (table 4); the coecient
of determination, very good (R
2
= 0.9987) (gure 1). The coecients
of determination, between real dry weights and those calculated with
Penman (1971) are the same for all eciencies, which is normal since
it is the change of a single variable in all cases (“common factor”)
which is the coecient ɛ.
The results show that it is possible to estimate sugar beet yields
with Penman (1971). The results with ɛ of 2.4 % and 3.77 % indicate
that it would be possible to work in the future with the gure of 1.9
%, which is not exact, since there is a distorting factor which is the
attack of nematodes. In this sense Hateld (2014) arms that, in
corn and soybean there are dierences in RUE in dierent years and
with dierent tillage methods in the soil. Lizana et al. (2021) indicate
that, in potato, nitrogen deciency reduces photosynthesis and RUE,
as does stress caused by nematodes. Therefore, although Penman
(1971) is useful for estimating sugar beet yields, it is necessary to
carry out experiments to nd the ɛ for each cultivar, taking into
account that the production factors must be found at the optimum,
avoiding nutrient, water, or phytosanitary stresses.
Conclusions
With AquaCrop, the calculated fresh weights are much lower
than the real ones. The calculated dry weights are similar to the actual
weights, although they do not follow the same trend, especially in
the ve-row treatment. AquaCrop is not suitable for estimating sugar
beet yields. It is necessary to calibrate the model with respect to the
crop itself, especially the harvest index (HI).
It is possible to estimate beet yields with the Penman (1971)
equation. It is necessary to carry out experiments to nd the solar
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Rev. Fac. Agron. (LUZ). 2024, 41(2): e244115 April-June. ISSN 2477-9407.6-6 |
radiation xation eciency (ɛ) with Penman (1971) for each cultivar,
considering good agronomic management to prevent any stress event.
Acknowledgments
To Prof. André Théwis and Mr. Dick Vermoote for making the
arrangements for the donation of the seed by Sesvanderhave, a
company to whom thanks are also due.
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