agriculture, ecosystems and environment46.20.115.203/download/cis/58013.pdf · jhabar mal sutaliya,...

Agriculture, Ecosystems and Environment 233 (2016) 325–335

Conservation agriculture-based wheat production better copes withextreme climate events than conventional tillage-based systems: Acase of untimely excess rainfall in Haryana, India

Jeetendra Prakash Aryal, Climate Economista,*,Tek Bahadur Sapkota, Mitigation Agronomista, Clare Maeve Stirling, Senior Agronomistb,M.L. Jat, Senior Cropping Systems Agronomista, Hanuman S. Jat, Senior Agronomistc,Munmun Rai, Senior Agronomista, Surabhi Mittal, Senior Agricultural Economista,Jhabar Mal Sutaliya, Senior Agronomistc

a International Maize and Wheat Improvement Center (CIMMYT), CG Block, National Agricultural Science Center (NASC) Complex, DPS Marg, Pusa Campus,New Delhi 110012, Indiab International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexicoc International Maize and Wheat Improvement Center (CIMMYT), CIMMYT, Karnal, Haryana, India

A R T I C L E I N F O

Article history:Received 6 February 2016Received in revised form 4 September 2016Accepted 13 September 2016Available online xxx

Keywords:Conservation agriculture-based wheatproduction systemConventional tillage-based wheatproduction systemClimatic extremesRainfall variabilityIndia

A B S T R A C T

This study explores whether conservation agriculture-based wheat production system (CAW) can bettercope with climatic extremes than the conventional tillage-based wheat production system (CTW). Toassess this, we used data collected from 208 wheat farmers in Haryana, India in 2013–14 (a period withnormal rainfall i.e., normal year) and 2014–15 (a period with untimely excess rainfall i.e., bad year) wheatseasons. Our analysis shows that whilst average wheat yield was greater under CAW than CTW duringboth bad and normal years, the difference was two-fold greater during the bad year (16% vs. 8%). Thisprovides new evidence that CAW can cope better with the climatic extremes, in this case untimely excessrainfall, compared to CTW. Absolute yield of the CAW and CTW was 10% and 16% lower in the bad yearcompared to the normal year, respectively. Extreme climate events, such as excess rainfall during wheatseason, can occur once in every four years in Haryana and result in a loss of income to both farmers,through a loss of yield, and the government, through compensatory payments to farmers. If, as targetedby the Haryana government in 2011, one million ha of wheat was brought under CAW, the state wouldhave produced an additional 0.66 million Mg of wheat in 2014–15, equivalent to US$ 153 million. This isan important finding given the increased vulnerability of wheat production to climatic variability in thisregion.

ã 2016 Elsevier B.V. All rights reserved.

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1. Introduction

Wheat plays a dominant role in global food security as itcontributes almost 20% of the total dietary calories and proteinsworldwide and almost 24% in South Asia (Shiferaw et al., 2013). InIndia, wheat is grown on about 29 million ha and is an importantcrop for food security. As India accounts for 12% of global wheatproduction, any loss of production in the region will have majorrepercussions for global food security (FAO, 2013). Wheatproductivity and total production in India increased tremendously

* Corresponding author.E-mail addresses: [email protected], [email protected] (J.P. Aryal).

http://dx.doi.org/10.1016/j.agee.2016.09.0130167-8809/ã 2016 Elsevier B.V. All rights reserved.

with the advent of green revolution (GR). However maintaining thegains of GR is increasingly a challenge with wheat yield in Indiahaving plateaued for last couple of years. Many factors such asdeclining soil fertility, degrading natural resources and increasingcost of production inputs are responsible for the recent stagnationof wheat yield, further compounded by the effects of climatechange and climatic variability.

Climatic variability in terms of rainfall (drought, excess rains)and terminal heat (i.e. high temperature during grain filling stage)severely impact on wheat production in India. For example, ifwheat is planted late, high temperature at the end of seasonhastens maturity and reduces grain yield (commonly known asterminal heat stress). One solution is to plant the wheat crop early

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Fig. 1. Distribution of rainfall (weekly total) over the wheat growing season for the years 2013–14 and 2014–15 and long-term average of weekly total (1982–2013). The errorbar in the long-term average shows the standard deviation. DAS = Days after sowing.

326 J.P. Aryal et al. / Agriculture, Ecosystems and Environment 233 (2016) 325–335

in November to escape terminal heat but this increases the risk ofexposure to heavy rainfall during late February and March (whichis largely unpredictable). Therefore, farmers need to adjust thetime of sowing as well as the wheat production system.

Adverse impacts of climatic variability on crop productivity hasbecome increasingly common in India. For instance, in 2004, due tohigh temperature wheat matured 10–20 days earlier than normalleading to a loss of more than 4 million Mg of wheat production(Samra and Singh, 2004). More recently in 2009–10, an abruptincrease in temperature during the grain filling stage of wheat wasassociated with an average yield loss of about 6% in north-eastIndia (Gupta et al., 2010). To ensure food security there is a need tofocus on resource-efficient technologies that maximizes crop yieldand increase adaptation to climatic variability.

Conservation agriculture-based wheat production system(CAW) is based on the principle of minimum soil disturbance,permanent soil cover and crop diversification (intercropping and/or rotation). In the rice-wheat system, CAW offers a means ofadvancing sowing date of wheat compared with the conventionaltillage-based wheat production system (CTW) due to the timesaved with direct seeding. The CAW was introduced in India in1990s as one of the resource-conserving technologies under theRice-Wheat Consortium (Harrington and Erenstein, 2005). Despitea plethora of studies to compare CAW with CTW in terms of soilproperties, productivity, resource use efficiency, economic profit-ability and environmental sustainability (Aryal et al., 2015c;Erenstein and Laxmi, 2008; Erenstein et al., 2008; Gathala et al.,2011; Gupta et al., 2010; Jat et al., 2014; Khatri-Chhetri et al., 2016;Sapkota et al., 2015), there is still a dearth of documented evidenceof the adaptive and risk-bearing capacity of CAW under climaticextremes.

This study assesses whether CAW better copes with untimelyrainfall compared with CTW and whether the yield benefits of CAWvary with farm size. We compare the yield under CAW and CTW ina bad year to assess the adaptive capacity of CAW. In this study,CAW refers to the zero tillage (ZT) based wheat production systemwhere residues from the previous crop (mainly rice residue in thiscase) are retained on the soil surface. We did not consider ZT in theabsence of residue retention. We selected the state of Haryana,

India as the study area for the following reasons. Firstly, CAW hasbeen practiced in Haryana for more than two decades andtherefore provided an appropriate region for a comparisonbetween CTW and CAW. Secondly, in the year 2014–15, wheatproduction in Haryana suffered severely due to untimely andexcessive rainfall (Global Watch and FAO, 2015). As a result, theGovernment of Haryana provided Indian rupees11092 crore (i.e. US$ 174.72 million) as compensation to farmers in 2015 (TheEconomic Times, 2015). Thirdly, in our informal discussions withfarmers in Haryana, most of them claimed that CAW suffered lessfrom untimely rainfall compared with CTW. All these factorscombined to provide a suitable set of conditions to test theadaptive response of CAW under ‘real life’ climatic extremes.

2. Effects of excess rainfall on wheat production

More than 80% of the total area under wheat production in Indiais irrigated (Kumar et al., 2004). Irrigation schedule for wheat ismore or less standardized. Farmers in western IGP generallyirrigate wheat 3–4 times depending on the management practicesand soil type. Usually the irrigations are timed at the crown rootinitiation stage (20–25 days after sowing, DAS), active tilleringstage (40–45 DAS), the flowering stage (70–75 DAS) and grainfilling stage (110–120 DAS). Winter rainfall during vegetativegrowth of wheat is generally beneficial but too much during thegrain filling to physiological maturity can be harmful. Farmersirrigate their crop on a routine basis but winter rains are largelyunpredictable and rainfall immediately after irrigation is the mostdetrimental as it results in prolonged water logging which resultsin a yellowing of leaves and stunted growth. The problem can becompounded by farmers over fertilizing the water-damaged crop,believing the yellowish-green crop stand is due to N deficiency.Excessive N application increases the susceptibility to diseases,pests and crop lodging. Water stagnation during grain-filling stagealso causes blackening of the wheat ear-head and loss of grain fill. Ifgrain filling occurs, grains are shriveled and light weighted, whichleads to substantial yield loss. Rainfall during maturity delays

1 The exchange rate for the year 2015 is: Indian rupees (INR) 1 = US$ 0.016

J.P. Aryal et al. / Agriculture, Ecosystems and Environment 233 (2016) 325–335 327

harvesting of wheat as farmers have to wait until the soilconditions and crop stand are suitable for operating the combineharvester. This will not only affect the wheat output but also affectsthe wheat procurement process. Rains during the ready-to-harvestperiod can increase the grain moisture content to above 14%making it unsuitable to sell directly from the field as per theexisting norms of Food Corporation of India.

In conventional systems, soils are generally puddled to restrictdrainage during the rice season but this creates a hard pan whichfurther restricts vertical water movement during the wheat seasoncreating conditions prone to waterlogging. By contrast, the ZTsystem in which residues from the previous crop are retained in thesoil not only help conserve moisture during drought conditions butalso enhance infiltration and percolation of water in the event ofexcess or untimely rainfall.

In the conventional system of production, wheat seeds arebroadcasted followed by roto-tillage and planking. This results insome seeds remaining on the surface while others are buried deepinto the soil. Whilst most seeds germinate well, those nearest thesurface may not develop a sufficiently deep rooted system that isable to withstand the impact of heavy rain and wind and so areprone to lodging. In ZT, on the other hand, seeds and fertilizer aredrilled at a consistent and optimal depth and row-geometryresulting into a well-developed root system (Singh et al., 2014) thatconfers greater resilience against adverse rainfall conditions.

3. Untimely rainfall and wheat crop loss in Haryana

In general the rice-wheat (RW) growing area of Haryana has asemi-arid subtropical climate, characterized by very hot summersand cool winters. Historical data indicates that there exists hugevariability in total annual rainfall ranging from 350 to 1400 mmwith average annual rainfall of about 750 mm, 75% of which isreceived during June–September based on long-term data fromCentral Soil Salinity Research Institute (CSSRI) Karnal, Haryana. Asthe wheat crop is grown during winter season (November–April),variability of monsoon rainfall has little effect on growth and yieldexcept at the end of the monsoon when rainfall affects wheatseeding. By contrast, variability in winter rainfall strongly affectsgrowth and production of wheat. Analysis of long-term weather

Fig. 2. Maximum, minimum and average weekly temperature during the wheat growingdotted vertical lines represent the grain development stage which is sensitive to high

data (1982–2013) from the meteorological department of CSSRIKarnal reveals that the total amount of rainfall received during thewheat season (29 November to 15 April) varies from 10 to 300 mm(average 110 mm). Not only is the amount of seasonal rainfallimportant but also the distribution in relation to crop growth withwheat performing better if rainfall is uniformly distributed overthe growing period rather than received in a few torrentialdownpours. In this respect, the 2013–14 and 2014–15 wheatseasons were considered normal and abnormal respectively basedon the seasonal rainfall distribution.

Total rainfall received during wheat season (29 October to 15April) in 2013–14 and 2014–15 was 170 and 235 mm, respectively(Fig. 1). Whilst rainfall in the 2013–14 wheat season was uniformlydistributed between the late vegetative to early maturity stage, inthe 2014–15 season 68% (160 out of 235 mm) of the total rainfallwas received only in two windows of one week duration each i.e.113–120 DAS (critical stage of grain development) and 149–155DAS (ready to harvest stage). In both years, terminal heat stress wasnot observed as temperature was below the threshold level of 33 �Cduring the grain filling stage (Fig. 2). Minimum and maximumtemperature for the wheat growing period was more favorable in2014–15 than in 2013–14 and yet the former was a bad year forwheat production in Haryana and this can be attributed more thananything to untimely and excess rainfall. Analysis of long-termweather data (1982–2015) from the study area reveals that badweather in terms of excess rainfall during critical stages of wheatoccurs one in every four years. Here, any year that received morethan 100 mm rainfall between 54 and 105 day of the year (graindevelopment and maturity period of wheat) was considered badyear.

Government of Haryana reported wheat yield loss of 5.8% in2014–15 compared with 2013–14 (Table 1) with the highest lossrecorded in the Karnal district (17.6% loss over 2013–14) duemainly to incessant rainfall over a few days during graindevelopment and ready-to-harvest stage.

4. Study area and data

For this study, we used the data collected from 208 wheatfarmers in 10 village clusters (scattered over 15 villages) in the

seasons in 2013–14 and 2014–15. In each growing season the window between twotemperature. DAS = Days after sowing.

Table 1Area, production, yield and percentage yield loss in wheat during 2013–14 and 2014–15.

State and district Area (million ha) Production (million Mg) Yield (Mg ha�1) Yield loss (%) over 2013–14

2013–14 2014–15 2013–14 2014–15 2013–14 2014–15

Haryana 2.499 2.54 11.8 11.3 4.722 4.45 5.8Karnal 0.172 0.174 0.845 0.704 4.912 4.046 17.6

Source: Department of Agriculture, Government of Haryana (http://www.agriharyana.nic.in accessed on 20 November 2015).


Karnal district, Haryana for two consecutive wheat seasons of2013–14 and 2014–15 (Fig. 3; Table 2).

Of the total number of farm households selected for the study,half of the households are selected from those who have adoptedCAW (i.e., adopters of CAW) and the remaining half was selectedfrom those who have not adopted it at all (i.e., non-adopters ofCAW). The data comprises of information on major householdcharacteristics, total operated land, land areas under CAW andCTW, production inputs, crop management and grain yield underCAW and CTW.

4.1. Descriptive statistics of the study households

Table 3 presents the characteristics of the sample households.Average age of household heads is about 40 years for both adoptersand non-adopters of CAW. All of the sample households reportedthat farming is their primary occupation. Very few farm house-holds have secondary occupations which include dairy, business,and rice mills. Average land holding size for adopters is 6.6 hacompared with only 4.8 ha for non-adopters of CAW. The majorityof the sample households have their own tractors. Among CAWadopted households, only five have their own ZT machine capableof seeding over previous crop residues (i.e. Turbo Happy Seeder).Therefore, most of the farmers rely on the custom hiring service forthe machines required for CAW. Of the total adopters, approxi-mately 30% of households have taken some kind of training inconservation agriculture.

5. Empirical framework of the study

The empirical framework of the study is as follows:

5.1. Analysis of major factors affecting wheat yield under normal andbad years

To test whether CAW better copes with extreme rainfallcompared with CTW, we classified all sample farm households intotwo categories (i.e., adopters of CAW and non-adopters of CAW)based on the type of wheat production system followed. Weestimated multiple regression model with dummy variable so thatwe can control for the impact of other inputs/farm managementelements that may affect wheat yield. The multiple regressionmodel with wheat production system dummy can be presented as:

y ¼ xb þ b1D1 þ e; e!N ð0; 1Þ ð1Þ

In Eq. (1), y is the wheat yield (Mg per ha), x is the vector of allother explanatory variables other than wheat production system(i.e., seed variety, number of irrigations applied, sowing dates,fertilizers applied, education of the household head, participationin agricultural training and access to credit for agriculturalactivities) and D1 refers to dummy variable which takes value 1if farmer has produced wheat using CAW and 0 otherwise. b and b1are coefficient vector and coefficient to dummy variable, respec-tively while eis the stochastic error term.

This analysis provides us with a basis to isolate the yielddifference due to other factors and hence, helps to attribute

whether the yield difference is due to differences in the wheatproduction systems. In addition, it also tested whether yield underCAW is higher than yield under CTW during a bad year, therebytesting the adaptive or climate risk coping capacity of CAW.

5.2. To test the yield difference between CA-based and conventionaltillage-based wheat production systems

We applied t-test of significance difference between twosample means to check whether there is difference between thesetwo alternative production systems. This test is carried out for boththe normal (2013–14) and bad year (2014–15).

We also used stochastic dominance analysis (SDA) to comparethe wheat yield distribution between CAW and CTW. In SDA, thecumulative distribution functions (CDFs) of yield of the wheat cropunder alternative systems are compared for the normal and badyear separately. Two major criteria for comparing the stochasticdominances are: first-order stochastic dominance (FSD) andsecond-order stochastic dominance (SSD). Assume that CAW(y)and CTW(y) are cumulative distribution functions of wheat yieldsfor CA-based and conventional tillage-based wheat productionsystems respectively. Under the FSD criterion, the distribution CAW(y) dominates CTW(y) if CTWðyÞ � CAWðyÞ � 0; 8y �

Fig. 3. Study area.


Table 2Villages under study and the distribution of sample size.

Villages Sample size

Anjanthali/Balu 28Badarpur/Dabkolikala 24Bastada/Kutail 14Chorpura 20Daha/Uncha Saman 18Gangar 20Padhana/Sandhir 24Pujam 20Shambli 20Taraori 20


For assessing the robustness of the results, we also estimatednon-parametric regressions to look at the association betweenyields and farm size under the two different wheat productionsystems in both normal and bad years. In general, non-parametricregression methods fit a local relationship between the dependentvariable y and the regressor x. The local relationship refers to theseparate fitted relationships that are obtained at different values ofx (Cameron and Trivedi, 2009).

Consider a local linear regression model:y ¼ m xð Þ þ u, where m(.) is the conditional mean function and x is a scalar. A localregression estimate of m(x) at x = x0 is a local weighted average of yi,i = 1, 2, . . . .,N, that places greater weight on observations while xiis closer to x0 and less weight on observations while xi is far from x0.This can be represented by:

m̂ x0ð Þ ¼XN

i¼1 wðxi; x0; hÞyiwhere w(xi, x0, h) represents the weight which decreases when thedistance between xi and x0 increases. As the bandwidth parameterh increases, more weight is placed on observations for which xi isclose to x0. The local linear estimator additionally includes a slopecoefficient and at x = x0 minimizes,XN

i¼1 Kxi � x0

h

� �yi � a0 � b0 xi � x0ð Þ

� �2

where K(.) is a kernel function that places greater weights on pointsxi is close to x0. The local linear estimator with degree ofpolynomial (t) greater or equal to 1 does much better than thepreceding methods at estimating m(x0) at values of x0 near theendpoints of the range of x, as it allows for any trends near the endpoints. Therefore, we used t ¼ 1 in our estimation. Of the several

Table 3Characteristics of sample households.

Household (HH) characteristics

Age of HH head (yr.) Illiterate HH head (no.) HH head with up to secondary education (no.) HH head with above secondary education (no.) Average land size (in ha) HHs with own tractor (no.) HHs with own laser land leveler (no.) HHs with own ZT machine (no.) HHs participated in CA training (no.) HHs participated in soil and water management training Access to credit required for agriculture Membership in farmer cooperatives Membership in any other institutions Know about climate change Have secondary occupation Total sample size

variants of nonparametric regressions, we estimated a localpolynomial regression, a variation of local regression, mainlybecause it better explains the variations in data (for details, seeCameron and Trivedi, 2009). We also checked kernel densities ofyield functions for these two alternative production systems andtested for the equality of the two distributions using two-sampleKolmogorov-Smirnov tests.

6. Results

6.1. Factors determining yield difference in normal and bad year

CAW had a positive and statistically highly significant effect onwheat yield compared to CTW in both bad and normal years(Table 4). In the normal year only one other variable – amount ofurea applied – was found to have a significant and positive impacton wheat yield, whereas in the bad year DAP and urea had anegative impact on yield. This is probably because, pale yellowishand stunted growth of plant due to water stagnation (in bad year)may have given an impression of under-fertilization and as a resultfarmers might have applied more fertilizer. We tested whetheramount of fertilizer applied was different between CAW and CTWin normal and bad year separately. We found no significantdifference in the application of fertilizer in both cases. This furtherhelps us to attribute CAW to observed yield differences.

Of the socio-economic variables, better educated farmers arefound to have slightly higher yields compared to illiterate farmersin both normal and bad years. Similarly, yields were higher forfarmers who had received agricultural training compared withthose that had not. Whilst there is always the option to add morevariables, the results presented in Table 4 justify the classificationof sample households in terms of adopters and non-adopters ofCAW.

6.2. Yield difference between CAW and CTW

Wheat yield was 8.1% higher in the CAW than CTW in thenormal year and this difference was found to be statisticallysignificant at 99% confidence level (Table 5). Of significance is theobservation that the yield advantages of CAW over CTW was muchhigher (15.6%) (and again statistically significant) in the 2014–15bad year. As far as we are aware, these results are the first of theirkind, providing clear evidence of the benefits of CAW in conferringresilience to climatic extremes. Another important finding is thatthe yield loss in the bad year (compared to normal year) was less inCAW (10.4%) compared with CTW (16.2%) indicating a greater yield

Adopters of CAW Non-adopters of CAW

39.4 40.59 1344 6451 276.6 4.885 715 014 031 25 0104 8637 11 0104 1045 0104 104

Table 4Factors explaining yield in normal and bad years.

Explanatory variables Normal year Bad year

Variety dummy (1 if H2967, 0 otherwise) �0.029 0.115(0.090) (0.087)

Production system dummy (1 if CAW, 0 if CTW) 0.392*** 0.617***(0.088) (0.082)

DAP (kg per ha) 0.007 �0.014***(0.007) (0.005)

Urea (kg per ha) 0.002** �0.003***(0.001) (0.001)

Number of irrigation applied �0.046 0.060(0.074) (0.121)

Potash (kg per ha) 0.005 0.002(0.003) (0.003)

Sulphur (kg per ha) �0.016 �0.008(0.010) (0.010)

Date of sowing �0.001 �0.004(0.006) (0.008)

HH head with up to secondary education (base category: illiterate) 0.094** 0.101***(0.045) (0.037)

HH head with above secondary education (base category: illiterate) 0.048*** 0.083***(0.014) (0.031)

Access to credit dummy (Yes = 1, No = 0) �0.129 0.114*(0.110) (0.067)

Participated in agricultural training (Yes = 1, No = 0) 0.043*** 0.065***(0.017) (0.023)

Constant 4.179** 3.993***(2.002) (1.369)

R-Squared 0.32 0.51No. of Observation 208 208

Note: *, **, and *** refer to 10%, 5%, and 1% level of significance, respectively. Standard errors are reported in parentheses.


penalty of the latter under climatic extremes. This was confirmedby the stochastic dominance analysis (Fig. 4) where it was possibleto use the results of the first-order analyses because the CDFs didnot intersect one another. In both years, CDFs for CAW were belowthat for CTW, indicating that the former dominated the later.

Table 5Wheat yield (Mg/ha) under alternative production systems in normal and bad year.

Wheat production system Average wheat yield (Mg/ha)

2013–14(Normal year)

2014–15(Bad year

CAW 5.46 4.89 (0.0517) (0.0558)

CTW 5.05 4.23 (0.0583) (0.0662)

Yield difference between CAW and CTW (Mg/ha) 0.41c 0.66d

t-test 5.19*** 7.59***

a yield loss due to climatic risk (untimely rain at the ready to harvest stage of wheab Yield loss due to climatic risk (untimely rain at the ready to harvest stage of wheac Yield gap between CAW and CTW in normal year i.e., in year 2013–14.d Yield gap between CAW and CTW in bad year (here, year with untimely rain at the re*** Refers to significant at 99% confidence level; observations are combined observat

However, the difference between CAW(y) and CTW(y) was larger inthe bad year, implying that CAW had a greater yield potential orlesser yield penalty in the bad year compared to CTW.

Yield difference between normal year and bad year (Mg/ha) t-test

)

0.57a 7.55***

0.82b 9.38***

t crop) in CAW between year 2013–14 and year 2014–15.t crop) in CTW between year 2013–14 and year 2014–15.

ady to harvest stage of wheat crop) i.e., year 2014–15 – address climatic variability.ions; standard errors are reported in parentheses.

Fig. 4. Stochastic dominance analysis of the wheat yield difference between CTW and CAW in a normal and bad year.


6.3. Yield variation across different farm sizes

Yield differences between CAW and CTW were higher andstatistically significant across all farm size categories in the badyear, whereas this difference was not statistically significant formarginal and small farmers in the normal year (Table 6).

The results of non-parametric regression (local polynomialregression analysis) is shown in Fig. 5. As compared to CAW, yieldvariation was higher in CTW in both normal and bad years and wasmuch higher among larger farms in the bad year (Fig. 5, rightpanel). This means shifting from CTW to CAW is more crucial forlarge farmers in the region and given their contribution tomarketable surplus, this adaptive response also has importantimplications for food security.

We also carried out the kernel density functions for the yieldfunctions under the CAW and CTW and used the Kolmogorov-

Table 6Yield variation across farm size in normal and bad year under CAW and CTW.

Land size (in ha) Yield (Normal year): 2013–14

CAW CTW Diff.

Marginal (�1) 5.63 5.25 0.38 (0.125) (0.211)

Small (>1 and �2) 5.62 5.11 0.52 (0.217) (0.135)

Semi-medium (>2 and �4) 5.53 5.193 0.34 (0.094) (0.081)

Medium (>4 and �10) 5.36 4.97 0.39 (0.078) (0.062)

Large (>10) 5.59 5.27 0.33 (0.103) (0.096)

Note: ** and *** refer to significant at 95% and 99% confidence levels respectively. Standaron the FAO (for details, visit http://www.fao.org/ag/agp/agpc/doc/counprof/India/India.

Smirnov test to confirm that differences in yield distributions werestatistically significant.

6.4. Farmers’ perception on why CAW performs better than CTW undervariable and untimely rainfall conditions

In response to the question whether or not you would like tocontinue with CAW, all of the adopters responded positively and allbelieved that this system copes better with variable and untimelyrainfall during the wheat season.

All farmers, who adopted CAW believe that a better root systemis the major reason why it copes better with untimely rainfall andalmost 35% of adopters considered that better water infiltration inthe CAW compared with CTW system reduces yield losses (Table 7).

t-test Yield (Bad year): 2014–15 t-test

CAW CTW Diff.

0.896 5.38 4.69 0.68 3.29***(0.125) (0.102)

1.53 5.07 4.19 0.87 2.68***(0.157) (0.143)

2.46** 5 4.26 0.75 5.22***(0.093) (0.085)

3.91*** 4.77 4.11 0.65 6.41***(0.087) (0.058)

2.29** 5 4.31 0.69 4.13***(0.099) (0.119)

d errors are reported in the parentheses. We classified the sample households basedhtm).

http://www.fao.org/ag/agp/agpc/doc/counprof/India/India.htm

Fig. 5. Yield variations across different farm sizes under two alternative wheat production systems.

Table 7Reasons why CAW copes with untimely rainfall.

Reasons why CAW copes better Adopters

Better water percolations 36Better root system 104Better fertilizer management 3Total sample size 104


7. Constraints to adoption of CAW

Table 8 presents the major constraints to adopt CAW reportedby the sample households.

Lack of machine availability is the major constraint to farmeradoption and this problem is also related with the type of ZTmachine that is required for sowing wheat with rice residue in thefield (Sidhu et al., 2007). In addition, 68% of non-adopters reportedthat lack of the knowledge is a major constraint to the uptake ofCAW by farmers, similar to the findings of Sapkota et al. (2015).

Table 8Major constraints to adopt CAW.

Constraints Adopters Non-adopters

Lack of machine availability 67 103Lack of knowledge 9 71Lack of credit facility 0 18Lack of confidence on CAW 1 13Total sample size 104 104

8. Discussions and policy implications

This study provides new and important evidence of the benefitsof conservation agriculture-based wheat production system (CAW)in terms of resilience to untimely rainfall during the wheat seasoncompared to conventional tillage-based wheat production system(CTW). Furthermore, CAW performs better under both normal andbad years. As farmers in many states in India face similar andincreased risks of climatic extremes such as untimely heavyrainfall, these findings have important implications for designingpolicies to improve adaptation of agriculture to climate change.The findings are discussed from the farmer’s and government’sperspectives.

8.1. Implications for farmers

Compared to CTW, the additional wheat yield obtained underCAW is 0.41 Mg ha�1 in a normal year and 0.66 Mg ha�1 in a badyear. This means a farmer can benefit from an additional amount ofUS$ 95 ha�1 (i.e., 0.41 Mg ha�1� US$ 232 Mg�1) in a normal yearand US$ 153 ha�1 (i.e., 0.66 Mg ha�1� US$ 232 Mg�1) in a bad yearif CAW is adopted. Given that the total production costs of CAW areless than that of CTW (Aryal et al., 2015b; Erenstein and Laxmi,2008), farmers stand to benefit more under CAW. Therefore, CAWhas both climate-adapted and economic benefits (in terms of yieldgain and total cost reduction), implying a win-win situation.Despite these benefits, farmer uptake of CAW is still relatively slow.Lack of knowledge and availability of machines required for CAWare two major reasons for low adoption. In addition, as CAW hasbeen adopted by the farmers in the study area for last two to fiveyears, it takes time to make full transition. Targeted policies toenhance farmer access to the Turbo Happy Seeder are already inplace in Haryana but uptake is still limited due to the constraints offarmer knowledge and confidence. Increasing farmers’ knowledge


on CAW and building their confidence requires targeted agricul-tural trainings focused on field demonstrations of CAW-relatedmachinery, together with regular interactions between research-ers and farmer societies. Local farmer clubs can play a crucial rolein initiating such programs.

8.2. Implications for government

Our analysis of long-term weather data (see Section 3) showsthat untimely excess rainfall events in the wheat season occursonce in 4 years implying that farmers in Haryana may face hugewheat crop losses due to untimely excess rainfall on a regular basis.As it is the liability of the government to help farmers when theircrops are damaged by the extreme climate events, this results in ahuge financial burden to the government. For example, Haryanagovernment spent about US$ 175 million on compensation towheat farmers who suffered from yield losses due to untimely andexcess rainfall in 2014–15 wheat season (The Economic Times,2015). Given this, it would be worthwhile to look for alternativecrop production systems that can reduce crop loss under suchextreme climatic events. It may also be argued that such fundswould have been better utilized in incentives schemes for theadoption of climate-adapted practices such as CAW.

In Haryana, 2.54 million ha (Mha) of land was under wheatproduction in 2014–15 (http://www.agriharyana.nic.in accessedon 20 November 2015). Haryana contributes almost 11% of thenational wheat production (HFC, 2012) and so any large scale lossof wheat production in the state exerts a huge financial burden onthe government of India. The Haryana state government has set atarget to increase the area of ZT wheat to one million ha by 2015(HFC, 2012). However, the target is not yet realized and at present,the area under ZT wheat is approximately 0.3 million ha (personalcommunication with Dr. Suresh Kumar Gehlawat, AdditionalDirector Agriculture (General), Department of Agriculture, Gov-ernment of Haryana). If one million ha of land under wheat inHaryana had been converted to CAW as targeted, the state couldhave produced almost 0.66 million Mg (0.66 Mg ha�1�1 Mha)additional wheat in the year 2014–15 (based on the results inTable 5). This additional production is equivalent to US$ 153million when estimated using the minimum support price forwheat in 2015 in India.

Institutional and policy reforms are required for the promotionof CAW as a means of adapting to climate change. Enhancingintegration between state-level plans and local level climatechange adaptation requirements is crucial for this (Aryal et al.,2015a). The Haryana state government has already initiated somepolicy change such as the provision of a subsidy to purchase themachine (Turbo Happy Seeder) required for CAW. Currently, thegovernment of Haryana is providing almost a 50% subsidy tofarmers to purchase Turbo Happy Seeder � INR 50000 (i.e., US$800) for male farmers and INR 63,000 (i.e., US$ 1008) for thefarmers with less than 3 acres and female farmers.

Proper evaluation and jointness of various agricultural subsidyprograms is an important issue in this context. For example,assuming that the “Turbo Happy Seeder” can serve approximately50 ha land per wheat season, the Haryana state requires 20,000units in order to bring one Mha of land under CAW. If thegovernment bears the full costs of purchasing 20,000 units, it willcost US$ 40 million in total (one unit cost US$ 2000). Given that theaverage life of the Happy Seeder machine currently available on themarket is approximately 9 years, the costs of total subsidy to thegovernment becomes approximately US$ 4.44 million yr�1.However, this shift will provide an additional wheat output of0.66 million Mg (i.e., 0.66 Mg ha�1�1 million ha) annually in a badyear and 0.41 million Mg (i.e., 0.41 Mg ha�1�1 million ha) annuallyin a normal year. Therefore, bringing 1 million ha of land under

CAW will yield an additional economic benefit of US$ 95.12 milliona year (i.e., 0.41 million Mg � US$ 232 Mg�1) in a normal year andUSD 153.12 million a year (i.e., 0.66 million Mg � US$ 232 Mg�1) ina bad year. This shows that there is a significant economic gaineven if the government provides a full subsidy to cover the costs ofpurchasing the Happy Turbo Seeder. However, the mechanism andpackaging of subsidies/incentives need to be carefully consideredto reduce any possible negative effects of subsidies and to optimizeits social benefits. Overall, the economic benefits that can berealized by investing in the equipment required for CAW is muchhigher than the total cost of the subsidy. This is a cruciallyimportant finding in terms of identifying economically viablestrategies to address climate risks in agriculture.

Strengthening public-private partnership and using localservice providers as the major information centers can improvefarmer’s knowledge on CAW. Hence, mobilizing the local farmcooperatives and local agricultural input/service providers inpartnership with the local governmental institutions related toagricultural technology extension services will help to increaseuptake of CA-based technologies.

9. Conclusions

This study has four main conclusions: i) the magnitude of yieldloss in wheat during a bad year was less in CAW than CTWproviding evidence that conservation agriculture-based practicesin wheat are an effective adaption response to excessive anduntimely rainfall events that are becoming more frequent inNorth-West India, ii) As CAW delivers yield advantages in bothgood and bad years, it is feasible to promote CAW even withoutsubsidy. However, increasing farmer’s knowledge and buildingtheir confidence on CAW through regular trainings are essential,iii) CAW can serve as climate risk adaptation measures irrespectiveof farm size, iv) analysis of long-term weather data from Haryanashowed that one in every four year can be bad year in terms ofextreme rainfall during wheat season, and thus, CAW can be a cost-effective means of adapting to rainfall variability during the wheatseason. Given the cost to the government of compensationpayments to farmers following the aftermath of adverse climateevents, effective management of subsidy and economic incentivesto adopt CAW is a critical issue. Lack of timely availability ofmachines, knowledge of and confidence in CAW are three majorconstraints to uptake by farmers. Therefore, there is a need toprovide a series of field-based workshops demonstrating CA-basedtechnologies to farmers and local service providers. Trainings andmobilization of local service providers together with agriculturalresearch institutions and universities can enhance furtheradoption of CAW whilst raising awareness amongst key decisionmakers of the evidence that now exists of the associated economicand adaptative benefits.

Acknowledgements

The authors acknowledge the financial support of CGIARresearch programs on Climate Change, Agriculture and FoodSecurity (CCAFS) and CRP Wheat for this study. We also sincerelyacknowledge the support from farmers of Haryana. Thanks also toall CIMMYT staffs based at Karnal for their contributions whilecollecting data, Shakshi Balyan, a research intern in CIMMYT-CCAFS, from Chaudhary Charan Singh University, Meerut forcollecting data required for the study and Love Kumar Singh inCIMMYT-CCAFS at Karnal, Haryana for his support during the fieldsurvey.


References

Aryal, J.P., Jat, M.L., Singh, R., Gehlawat, S.K., Agarwal, T., 2015a. Framework,guidelines and governance for designing local adaptation plan of action tomainstream climate-smart villages in India. International Maize and WheatImprovement Center (CIMMYT), New Delhi, India.

Aryal, J.P., Mehrotra, M.B., Jat, M.L., Sidhu, H.S., 2015b. Impacts of laser land levelingin rice-wheat systems of the north-western indo-gangetic plains of India. FoodSecur. 7, 725–738. doi:http://dx.doi.org/10.1007/s12571-015-0460-y.

Aryal, J.P., Sapkota, T.B., Jat, M.L., Bishnoi, D.K., 2015c. On-farm economic andenvironmental impact of zero-tillage wheat: a case of North-West India. Exp.Agric. 51, 1–16. doi:http://dx.doi.org/10.1017/S001447971400012X.

Cameron, C.A., Trivedi, P.K., 2009. Microeconometrics Using Stata. Stata Press, Texas,USA.

Erenstein, O., Laxmi, V., 2008. Zero tillage impacts in India’s rice-wheat systems: areview. Soil Tillage Res. 100, 1–14. doi:http://dx.doi.org/10.1016/j.still.2008.05.001.

Erenstein, O., Farooq, U., Malik, R.K., Sharif, M., 2008. On-farm impacts of zero tillagewheat in South Asia’s rice-wheat systems. Field Crops Res. 105, 240–252. doi:http://dx.doi.org/10.1016/j.fcr.2007.10.010.

FAO, 2013. Food and agricultural commodities production: countries by commodity[WWW Document]. URL www.faostat.fao.org (accessed 5.13.15).

Gathala, M.K., Ladha, J.K., Kumar, V., Saharawat, Y.S., Kumar, V., Sharma, P.K., Sharma,S., Pathak, H., 2011. Tillage and crop establishment affects sustainability of SouthAsian rice-wheat system. Agron. J. 103, 961. doi:http://dx.doi.org/10.2134/agronj2010.0394.

Global Watch, FAO, 2015. Global information and early warning system on food andagriculture [WWW Document]. GIEWS Ctry. Br. India. URL http://www.fao.org/giews/countrybrief/country/IND/pdf/IND.pdf (accessed 10.12.15).

Gupta, R., Gopal, R., Jat, M.L., Jat, R.K., Sidhu, H.S., Minhas, P., Malik, R., 2010. Wheatproductivity in Indo-Gangetic Plains of India during 2010: terminal heat effectsand mitigation strategies. Conserv. Agric. 14, 1–3.

HFC, 2012. Working Group Report on Conservation Agriculture for Sustainable CropProduction in Haryana. Haryana Farmers’ Commission (HFC). CCS HaryanaAgricultural University, Hisar, India.

Harrington, L., Erenstein, O., 2005. Conservation agriculture and resourceconserving technologies – a global perspective. In: Abrol, I.P., Gupta, R.K., Malik,

R.K. (Eds.), Conservation Agriculture – Satus and Prospects. Centre forAdvancement of Sustainable Agriculture, NASC Complex, New Delhi, India, pp.1–12.

Jat, R.K., Sapkota, T.B., Singh, R.G., Jat, M.L., Kumar, M., Gupta, R.K., 2014. Seven yearsof conservation agriculture in a rice-wheat rotation of Eastern Gangetic Plains ofSouth Asia: yield trends and economic profitability. Field Crops Res. 164, 199–210. doi:http://dx.doi.org/10.1016/j.fcr.2014.04.015.

Khatri-Chhetri, A., Aryal, J.P., Sapkota, T.B., Khurana, R., 2016. Economic benefits ofclimate-smart agricultural practices to smallholder farmers in the Indo-Gangetic Plains of India. Curr. Sci. 110, 1244–1249. doi:http://dx.doi.org/10.18520/cs/v110/i7/1244-1249.

Kumar, K.K., Kumar, R.K., Ashrit, R.G., Deshpande, N.R., Hansen, J.W., 2004. Climateimpacts on Indian agriculture. Int. J. Climatol. 24, 1375–1393. doi:http://dx.doi.org/10.1002/joc.1081.

Mas-Colell, A., Whinston, M.D., Green, J.R., 1995. Microeconomic Theory. OxfordUniversity Press, New York.

Samra, J.S., Singh, G., 2004. Heat Wave of March 2004: Impact on Agriculture. IndianCouncil of Agricultural Research, New Delhi, India.

Sapkota, T.B., Jat, M.L., Aryal, J.P., Jat, R.K., Khatri-Chhetri, A., 2015. Climate changeadaptation, greenhouse gas mitigation and economic profitability ofconservation agriculuture: some examples from cereal systems of Indo-Gangetic Plains. J. Integr. Agric. 18, 1524–1533. doi:http://dx.doi.org/10.1016/S2095-3119(15)61093-0.

Shiferaw, B., Smale, M., Braun, H.-J., Duveiller, E., Reynolds, M., Muricho, G., 2013.Crops that feed the world 10. Past successes and future challenges to the roleplayed by wheat in global food security. Food Secur. 5, 291–317. doi:http://dx.doi.org/10.1007/s12571-013-0263-y.

Sidhu, H.S., Humphreys, E., Dhillon, S.S., Blackwell, J., Bector, V., 2007. The HappySeeder enables direct drilling of wheat into rice stubble. Anim. Prod. Sci. 47,844–854.

Singh, A., Phogat, V.K., Dahiya, R., Batra, S.D., 2014. Impact of long-term zero tillwheat on soil physical properties and wheat productivity under rice-wheatcropping system. Soil Tillage Res. 140, 98–105. doi:http://dx.doi.org/10.1016/j.still.2014.03.002.

The Economic Times, 2015. Haryana government announces Rs 1092 crore farmercompensation. Chandigarh, India.

http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0005http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0005http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0005http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0005http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0010http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0010http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0010http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0015http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0015http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0015http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0020http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0020http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0025http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0025http://dx.doi.org/10.1016/j.still.2008.05.001http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0030http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0030http://dx.doi.org/10.1016/j.fcr.2007.10.010http://www.faostat.fao.orghttp://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0040http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0040http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0040http://dx.doi.org/10.2134/agronj2010.0394http://www.fao.org/giews/countrybrief/country/IND/pdf/IND.pdfhttp://www.fao.org/giews/countrybrief/country/IND/pdf/IND.pdfhttp://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0050http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0050http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0050http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0055http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0055http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0055http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0060http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0060http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0060http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0060http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0060http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0065http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0065http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0065http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0065http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0070http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0070http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0070http://dx.doi.org/10.18520/cs/v110/i7/1244-1249http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0075http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0075http://dx.doi.org/10.1002/joc.1081http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0080http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0080http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0085http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0085http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0090http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0090http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0090http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0090http://dx.doi.org/10.1016/S2095-3119(15)61093-0http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0095http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0095http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0095http://dx.doi.org/10.1007/s12571-013-0263-yhttp://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0100http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0100http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0100http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0105http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0105http://refhub.elsevier.com/S0167-8809(16)30464-9/sbref0105http://dx.doi.org/10.1016/j.still.2014.03.002

Conservation agriculture-based wheat production better copes with extreme climate events than conventional tillage-based s...1 Introduction2 Effects of excess rainfall on wheat production3 Untimely rainfall and wheat crop loss in Haryana4 Study area and data4.1 Descriptive statistics of the study households

5 Empirical framework of the study5.1 Analysis of major factors affecting wheat yield under normal and bad years5.2 To test the yield difference between CA-based and conventional tillage-based wheat production systems5.3 To test the yield variation across different farm sizes

6 Results6.1 Factors determining yield difference in normal and bad year6.2 Yield difference between CAW and CTW6.3 Yield variation across different farm sizes6.4 Farmers’ perception on why CAW performs better than CTW under variable and untimely rainfall conditions

7 Constraints to adoption of CAW8 Discussions and policy implications8.1 Implications for farmers8.2 Implications for government

9 ConclusionsAcknowledgementsReferences

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