CROP YIELD RESPONSE to water

WATER TECHNOLOGY CENTRE,PJTSAU
Presentation by: Submitted to:
K . Archana Dr. T. L. Neelima
RAM/2020-107 Scientist and professor(Agronomy)
Course no:AWM-503
Crop water requirement and irrigation planning

INTRODUCTION:
 Food production and water use are inextricably linked.
 Water has always been the main factor limiting crop production in much of the world
where rainfall is insufficient to meet crop demand.
 With the ever-increasing competition for finite water resources worldwide and the
steadily rising demand for agricultural commodities, the call to improve the efficiency and
productivity of water use for crop production, to ensure future food security and address
the uncertainties associated with climate change, has never been more urgent.
 To examine the pathways for increasing the efficiency and productivity of water use, the
yield response of crops to water must be known.

•IMPORTANCE OF WATER IN PLANT LIFE
 Photosynthesis
 Transpiration
 Translocation
 Enzymatic action
 Hormonal activity
 As an agent for dissolution of plant nutrients.
 As a integral part of decomposition
 Germination
 Microclimate effect

•RELATIONSHIP BETWEEN YIELD AND WATER:
• When water supply is not limiting ET can be expected at maximum rate to attain full potential
yield provided, no other constraining factor interfere (Hillel 1987).
• When water is limiting, water use may fall below ETm.
• An empirically based equation to predict yield from known values of ET was given by Stewart
(1977) for dry matter production.
Y/Yx = (1-b) + b.(ETA /ETx )
Y: dry matter yield
Yx: maximum attainable yield
ETA : actual evapotranspiration
ETx : maximum evapotranspiration
b: slope of relative yield versus the ET deficit (ETD)
(ETD) = 1 - (ETa / ETm)

• Functional relationship between water and yield is necessary to develop production
functions.
• This relation ship is usually obtained by subjecting experimental data to Regression
Analysis.
• Complete water production function involve the physiological response of crops to water
supply as well as economic principles.
• In the relationship of water and yield not only the quantity of water applied and but also
time of crop growth stage is also important.
• Functional relationship between water and yield is obtained by establishing relationship
between applied water or evapotranspiration or transpiration to either growth or yield.
• The amount of water transpired is good measure of yield but difficult to measure .
• Evapotranspiration is next best parameter to estimate yield.
• Applied water gives less accurate relationship with yield but easy to measure.

 Linear relationship between crop yield and ET was obtained by several scientists . stewart
developed generalized production function.
Where,
Ya is the actual yield , ETa the actual ET, Ym the maximum yield , ETm for maximum ET and Ky
is constant.
• Calculation Procedures
• The calculation procedure for Equation 1 to determine actual yield Ya has four steps:
• i. Estimate maximum yield (Yx) of an adapted crop variety, as determined by its genetic
makeup and climate, assuming agronomic factors (e.g. water, fertilizers, pest and diseases)
are not limiting.
• ii. Calculate maximum evapotranspiration (ETx) according to established methodologies
and considering that crop-water requirements are fully met.
• iii. Determine actual crop evapotranspiration (ETa) under the specific situation, as
determined by the available water supply to the crop.
•

THE YIELD RESPONSE FACTOR(Ky):
• The Ky values are crop specific and vary over the growing season according to growth
stages with:
 Ky>1: crop response is very sensitive to water deficit with proportional larger yield
reductions when water use is reduced because of stress.
 Ky<1:crop is more tolerant to water deficit , and recovers partially from stress , exhibiting
less than proportional reductions in yield with reduced water use.
 Ky=1:yield reduction is directly proportional to reduced water use.
• iv. Evaluate actual yield (Ya) through the proper selection of the response factor (Ky) for the
full growing season or over the different growing stages.

• Yield response will differ largely depending on the stage the water stress occurs.
• Typically flowering and yield formation stages are sensitive to stress, while stress
occurring during the ripening phases has a limited impact, as in the vegetative phase,
provided the crop is able to recover from stress in subsequent stages.

•Factors affecting yield response factor
 Water Availability
 Crop Variety
 Life stage of the crop
 Fertilizer
 Salinity
 Pests and diseases
 Agronomic or management practices
 To determine optimum input combinations to obtain higher yields –Production
functions are very important.

 Production function : the production function or yield response curve is a non-negative
mathematical function relating the quantities of inputs employed to the quantity of output
produced.
 It presupposes technical efficiency and provides the maximum output obtainable for the
various combinations of inputs , thus constituting a boundary between attainable and
unattainable output.
 In its simplest form , yield or output is a function of one variable such as water, which is
stated by the equation:
Y=f(X1)
Where Y is yield and X1 is water applied to the crop. Of course , other factors are necessary
for production . To include additional factors the function is written:
Y=f(X1, X2 , X3 ,…….,Xn)

• Where X1 is water, X2 may be plant population per acre , X3 may be nitrogen fertilizer
applied, and all the X’s up to Xn are other independent variables which contribute to the
total yield of the crop.
• Type of production function:
1. It is possible that the amount of product increases by the same amount for each
additional unit of input,in this case it is said that there are “constant returns” in the
output as the input varies in the production of a particular commodity.
2. Each additional unit of input results in a larger increase in product than the preceding
unit. When this is true it is said that there are “ increasing returns” from the input.
3. Each additional unit of input results in a smaller increase in product than the preceding
unit. Thus it is said that there are “decreasing” or “diminishing returns”. This case is the
one normally expected in the production of agricultural product.

 Production function is the focus of efficient points of the feasible production set.
 It is quantitative relationship indicating maximum of physical product obtainable from
specified quantities of a set of inputs , given the existing technology governing the input-
output relationships.
 The three most important inputs in irrigation farming are water , plant population and
fertilizers .
 A combination of optimum levels of each of these three inputs leads to maximum yield of
the crop with minimum cost.
 Synergy or mechanism which makes the whole something very much more than the sum
of parts , is a potent tool in nature for getting large effect from small resources .
 The release of synergistic interactions provides best possible means of enhancing the
cost-benefit ratio.

 Yield from water will interact with key inputs as well as soil and climatic variables.
 Highest yield and water use efficiency is possible only through optimum levels of all these
factors in combination and not individually.
 The role of irrigation water, therefore ,lies not only in its own productivity but also in
increasing the productivity of other associated key inputs.
 Irrigation scheduling is commonly planned on a complete elimination of water deficits.
 Thus in years of short supply of water , irrigated hectarage is reduced so that the water
requirement for the crop on each hectare of land can be met in full.
 Planning of this type is increasingly inadequate as water shortages become more common
and irrigation prices rise.
 When water is scarce and crop production is much needed , the objective has to be shifted
from maximum net profit to maximum WUE or from the concept of potential crop yields to
optimum yields with maximum WUE.
 Net profit is maximum at some irrigation level below that associated with maximum yield.

Computerized calculation procedures (CROPWAT):
• The use of the water production functions , is facilitated using the CROPWAT model that
provides computation procedures to determine yield reductions based on the approach
using daily water balance calculations.
• CROPWAT has been widely used as a practical management tool for irrigation scheduling
and to estimate yield reductions under water deficit condition.
• Standard values for crop parameters (Kc, p, rooting depth, etc.) and Ky values are
included in the model and can be modified to adjust to local conditions.
• CROPWAT includes various modules to calculate reference evapotranspiration from daily,
decade or monthly climatic data, crop-water requirements and irrigation water
requirements from climatic and crop data, as well as scheme water supply for varying
cropping patterns.
• CROPWAT was designed as a practical tool to carry out standard calculations for design
and management of irrigation schemes, and for improving irrigation practices. It may
also be used for irrigation scheduling under full or deficit irrigation conditions and for
this, it uses the yield response factors derived from the crop-water production functions

o To develop a comprehensive system for planning the use of water in agriculture , functional
relationships between crop yield and irrigation water, plant population and fertilizers singly
and in combination are necessary .
o The rate of substitution among water, fertilizer, plant population and land have direct
application to resource allocation.
o Production functions help to determine the following:
a) Over what area a given water supply should be allocated.
b) How much should be allocated to areas with soils that differ in characteristics and
productivity.
c) Optimal amount of water to be used per hectare under either limited or unlimited
supplies.
d) Optimal combination of inputs or resources in production.
e) The degree of substitutability or complementarity between inputs
f) The focus of the least cost-input mix for an expansion in the output level .

• Response surface is an experimental procedure for exploring and examining the nature
of responses obtained from the simultaneous variation of quantitative factors .
• The isoquant represent a consistent amount of output . the isoquant is known
alternatively as an equal product curve or a production indifference curve.
• The multi variable(two) continuous production function when quantified , generates a
three dimensional response surface .
• A response surfaces is given in fig;.
• A typical isoquant (line of same yield) is super imposed in the figure.
• It gives all possible combinations of the two inputs for obtaining a given level of
output(yield).
• For instance one such combination is shown by arrow marks form point A on each of the
response surfaces.

RESPONSE SURFACE

 A typical family of isoquants are also calculated from production function and plotted in
two dimensional planes for constant levels of yield by solving equations for different values
of X1 and X2.
 The slope at each point on isoquant represent the rate at which one input must be
substituted for the other to maintain a constant level of output.
ISOQUANTS

• The isoquants, which are easier to read than response surface can be used for determining
optimum combination of different inputs.
• Thus if one input is scarce and costly it can be substituted by easily available and cheaper
input to get anticipated yield.
• This aspect is illustrated in fig: where an anticipated yield of 67 q/ha can be obtained either
by (A) 52 kg N/ha and 225 cm of irrigation water or (B) 72 kg/ha and 205cm of irrigation
water.
• Isoquants also reveal that optimum nitrogen is 90 kg/ha in summer season giving grain
yield of 68.5 q/ha . Beyond these nitrogen levels there will be decreasing response.

CONCLUSION:
Finally we can conclude that:
• In order to find a optimum combination of inputs to obtain outputs efficiently
“isoquants” are very useful .
• Isoquants can be used easily than response surfaces.
• Through this graphs ,many input combinations can be obtained to obtain a similar
output .
• To obtain efficient input combinations to get higher yields –production function is very
important.

Article
 Estimation of Yield Response Factor for Each Growth Stage under Local Conditions Using
Aqua Crop-OS:
Mathias Kuschel - Otarola , Niels Schutze , Eduardo Holzapfel , Alex Godoy-Faúndez ,
Oleksandr Mialyk and Diego Rivera (Published : 10 APRIL , 2020).
 They proposed a methodology to estimate the yield response factor (i.e., the slope of the
water-yield function) under local conditions for a given crop, weather, sowing date, and
management at each growth stage using AquaCrop-OS. The methodology was applied to
three crops (maize, sugar beet, and wheat) and four soil types (clay loam, loam, silty clay
loam, and silty loam), considering three levels of bulk density: low, medium, and high.
Yields are estimated for different weather and management scenarios using a problem-
specific algorithm for optimal irrigation scheduling with limited water supply (GET-OPTIS).
 Our results show a good agreement between benchmarking (mathematical approach) and
benchmark (estimated by AquaCrop-OS) using the Normalised Root Mean Square Error
(NRMSE), allowing us to estimate reliable yield response factors (Ky) under local conditions
and to dispose of the typical simple mathematical approach, which estimates the yield
reduction as a result of water scarcity at each growth stage.

Yield Response Factor For Maize
The Ky values for maize at each growth stage and for soils with low, medium , and high bulk
densities. According to Steduto et al., Ky > 1 implies that the crop is very sensitive to water
deficits, Ky < 1 means that it is more tolerant to water deficits, and Ky = 1 corresponds to a
direct proportion of yield reduction to reduced water use. The Ky value for the first growth
stage was close to zero for all soil types. On the other hand, Ky always reached its maximum
in the third growth stage (flowering). Its values ranged from 0.9 to 1.4, indicating that maize
is very sensitive to water deficits in this stage. Thus, water stress during this stage incurs
larger reductions than in other stages . For clay loam soil, the higher the bulk density, the
lower the value of Ky was in the third growth stage. With the exception of the fourth growth
stage, the obtained values were lower than those proposed by the FAO [51].

Yield Response Factor for Sugar Beet
The Ky values for sugar beet at each growth stage and for each soil type. Similarly to the
results obtained for maize , the Ky value in the first growth stage was close to zero for all soil
types, indicating that yield is not affected when there is enough water in the soil profile. It
reached its maximum value in the third growth stage for most soil types; except for clay loam
soil with high bulk density, where this value was the lowest (compared with the other soil
types).
Yield Response Factor For Wheat
the Ky values for wheat for each growth stage and for each soil type. Similarly to the results
obtained for maize and sugar beet , the Ky value in the first growth stage was close to zero,
except for loam soil with medium bulk density (Ky = 0.15). Regarding the value for the third
growth stage, this value was relatively low when compared to maize and sugar beet. The
lowest value for the third growth stage was presented in loam soil with medium bulk density
(Ky = 0.73); this soil type also presented a value over the 70th percentile for the second
growth stage (Ky = 0.30). With the exception of the third growth stage, the obtained values
were lower than those proposed by the FAO [51]. Soils with high bulk density showed lower
differences, with respect to the values proposed in the literature.

Conclusions
They developed and assessed a methodology to estimate the Ky value under local conditions
for a given crop, soil, weather, sowing date, and management, as well as for each growth
stage , Water 2020, 12, 1080 12 of 14 using AquaCrop-OS under Chilean conditions. The
proposed methodology presented a good agreement; excellent simulation of 67%, 35%, and
82% was observed for maize, sugar beet, and wheat, respectively , allowing us to estimate
the Ky values under local conditions and to dispose of the typical simple mathematical
approach in which yield reduction is estimated as a result of water scarcity at each growth
stage. Most irrigation managers consider resources to be available over the whole season.
However, at sub-seasonal time scales—weekly, monthly, or even daily—irrigation managers
and farmers must make decisions and take action based on new information regarding
climate drivers and resource availability. Thus, the Ky value could be used to tailor water
management strategies under changing conditions . Future studies should focus on the
estimation of Ky under a more diverse range of management scenarios.
Yield response article

Article
Yield Response Factor for Onion (Allium Cepa L) Crop Under Deficit Irrigation in Semiarid Tropics of
Maharashtra
The present study deals with the study of yield response factor (Ky) for onion crop cultivated under
deficit irrigation for Rahuri region (Maharashra). The field experiment was conducted to determine the
yield response factor of the onion (Allium cepa L.) cv. N-2-4-1 crop under the deficit irrigation approach
during summer season of 2012 and 2013 at Instructional Farm of the Department of Irrigation and
Drainage Engineering, Dr. Annasaheb Shinde College of Agricultural Engineering, Mahatma Phule
Krishi Vidyapeeth Rahuri. Experiment was carried out in Randomized Block Design (RBD) with 27
treatments and two replications based on different combinations of the quantity of water stress during
different crop growth stages. Water applied per irrigation and soil moisture contents before and after
irrigation were monitored throughout the season, while onion bulbs were harvested at the end of
season and weighed. Average daily crop water use (crop consumptive use) were estimated from the
soil moisture content using the soil moisture depletion method.

• Crop yield response factor (Ky) indicates a linear relationship between the decrease in relative water
consumption and the decrease in relative yield. It shows the response of yield with respect to the
decrease in water consumption. In other words, it explains the decrease in yield caused by the per
unit decrease in water consumption.
• The moisture content observations during 2012 and 2013 were recorded before irrigation, after
irrigation and during irrigation period for all the treatments for the purpose of computing the actual
evapotranspiration. The treatment T1 was treatment without water stress and hence actual
evapotranspiration of treatment T1 was considered as maximum crop evapotranspiration. The
maximum crop evapotranspiration during 2012 and 2013 and average of 2012 and 2013 were
computed. These are 529, 556 and 543 mm for 2012, 2013 and average of 2012 and 2013
respectively. The treatments T2 to T27 were treatments with some stress. The values of actual
evapotranspiration along with maximum onion evapotranspiration are presented in Tables 3,4 and 5.
These tables show the relative decreases in seasonal crop water use and bulb yield for onion crop
during 2012 and 2013 seasons and average of two seasons.

• The relationship between relative yield reduction and relative evapotranspiration deficit
for onion yield . The yield response factor (Ky) for onion in 2012, 2013 and average of
2012 & 2013 by regression analysis was found to be 1.58, 1.48 and 1.54 for whole
growing season. Result obtained was in agreement with those reported by Doorenbos
and Kassam (1986). They reported that seasonal yield response factor (Ky) value of 1.50
for onion during the whole growing season. Generally, higher Ky values indicate that the
crop will have a greater yield loss when the crop water requirements are not met. This
result indicated a high impact of soil-water stress treatment on the onion yield.
Therefore, water management of onion is extremely important at all stages of plant
growth.
Conclusion
• The results indicated a high impact of soil-water stress treatments on the onions yield.
• The crop water use of the onion crop decreased with increase in irrigation deficit.
• The yield response factor (Ky) for onion in semi arid tropics of Maharashtra was found to
be 1.54 for whole growing season.
• Yield response in onion

Article
Determination of the yield response factor for field crop deficit irrigation
Najarchi , M. , Kaveh, F. , Babazadeh , H. and Manshouri , M.(published 31 march 2011)
• A comparison between our data and data from FAO (1979) was presented in Table .
According to this recent table, calculated data were different with data presented. The
results of this research do compare well with Ky computed by Andrioli and Sentel has
(2009). For maize and our Ky for winter wheat also compare well to those presented by
Moutonnet (2001). The Ky value of the maize for total growing period was higher than
1.25 reported by Doorenbos and Kassam (1994). Also, this value was higher than the
ones determined by Dagdelen et al. (2006) and Mengu and Ozgurel (2008) in Turkey,
which ranged from 0.99 to 1.04. However, the obtained value in the present study was
close to that observed by Igbadun et al. (2006) in Tanzania (1.90), and by Payero et al.
(2008) in Nebraska, USA (from 1.54 to 1.74). The regression was analysis using SPSS16
PC software and the results were presented in Tables 8 and 9 respectively

Conclusions
• Based on this comparative analysis, the average Ky value calculated in this research was
higher than the values reported by FAO (1979). Consequently, the reductions in yield
through deficit irrigation are higher than those reported by FAO (1979). Data sets used in
this research should be expanded using more well managed field experiments on different
soils and in different climatic conditions.
• Yield response factor for field crops article
• Yield , water use efficiency, and yield response factor in carrot crop under deficit irrigation
depths
• A proposed method to determine yield response factors of different crops under deficit
irrigation using inverse formulation approach

• Agronomy journal ,L. F. Welch, W. E. Adams, and J. L. Carmon’ published in January,1963
• Agricultural water management ( 5 march 2014)
• Crop production Ciência Rural, Santa Maria, (jul, 2016)
• African Journal of Agricultural Research ·( August 2011)
• N.K. Garg, S.M. Dadhich / Agricultural Water Management 137 (2014)
• Current agriculture research journal (30 October 2015)
• http://www.mdpi.com/journal/water (10 April 2020)

CROP YIELD RESPONSE to water

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