Resilience of smallholder cropping to climatic variability

Journal Pre-proof Resilience of smallholder cropping to climatic variability

Prahlad Lamichhane, Kelly K. Miller, Michalis Hadjikakou, Brett A. Bryan PII:

S0048-9697(20)30974-8

DOI:

https://doi.org/10.1016/j.scitotenv.2020.137464

Reference:

STOTEN 137464

To appear in:

Science of the Total Environment

Received date:

3 December 2019

Revised date:

19 February 2020

Accepted date:

19 February 2020

Please cite this article as: P. Lamichhane, K.K. Miller, M. Hadjikakou, et al., Resilience of smallholder cropping to climatic variability, Science of the Total Environment (2020), https://doi.org/10.1016/j.scitotenv.2020.137464

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© 2020 Published by Elsevier.

Journal Pre-proof

Resilience of smallholder cropping to climatic variability Prahlad Lamichhane*1, Kelly K. Miller1, Michalis Hadjikakou1, Brett A. Bryan1 1

Centre for Integrative Ecology (CIE), School of Life and Environmental Sciences, Deakin University,

Burwood, Victoria, Australia +61 4 66 064 795

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*Corresponding author email: [email protected]

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Abstract Smallholder agriculture is a major contributor to global food production and is vital for ensuring food security in many developing countries. Smallholder agriculture is a typically complex and heterogeneous socialecological systems that is especially susceptible to climatic variability. Research has often focused on examining climate impacts on crops in smallholder agriculture. However, the resilience of smallholder agriculture in terms of maintaining yield remains largely unexplored. We empirically quantified the resilience of rice, wheat and maize to climatic variabilities for the Far Western Province of Nepal. We calculated resilience indices (RI) comparing the anomalies of actual yield in agricultural statistics to the expected yields

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generated by process-based yield simulation model for nine districts across the Terai, Hill and Mountain

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regions of the province. Based on the sustainable livelihoods framework, we then correlated capital indicators

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with resilience to assess the capacity of indicator variables to explain resilience. The results demonstrate the variability of resilience across regions and crops. Terai, Hill and Mountain regions were found to be resilient

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in wheat, rice and wheat, and maize, respectively. Each region has maintained resilience in at least one crop

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suggesting that smallholder farmers have prioritised food subsistence. While Nepal’s current Agricultural Development Strategy is focused on boosting yields in the Terai, we found the region to be less resilient

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overall compared to the Hill and the Mountain regions. Theory-driven capital indicators exhibited a weak and

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often contradictory relationship with resilience. Such indicators, used in isolation, could therefore misguide the resilience assessment in the absence of complementary fine-scale exploratory social research necessary to explain the drivers of resilience in smallholder agriculture and infer policy decisions. Key words: climate change, agriculture, cereal crops, developing country, resilience, Nepal

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Introduction

Smallholder agriculture, characterised by landholdings of less than two hectares, constitute 84% of farms globally (Lowder et al., 2016). In regions of smallholder dominance such as South and South-East Asia and Sub-Saharan Africa, smallholder agriculture contributes over 70% of the food calories produced (Samberg et al., 2016). Smallholder farming typically occurs within a complex social-ecological system where farmers have been harnessing opportunities and thriving amidst complex and difficult socio-political and environmental dynamics (West and Haug, 2017). Smallholder agriculture is often characterized by extreme poverty and severe food insecurity (Brown et al., 2019). Cereals, especially rice (Oriza S.), wheat -2-

Journal Pre-proof (Triticum L.) and maize (Zea Mais L.), are widely grown by smallholder farmers for subsistence (Waddington et al., 2010). Climate change has impacted crop production and food security in regions of smallholder dominance (Eissler et al., 2019; Hussain et al., 2019). Crop responses to climate variability and change are variable but have mostly been detrimental (Krishnan et al., 2011; Lobell et al., 2007). Additionally, the impacts of climatic perturbations on major cereal crop yields have shown pronounced spatial heterogeneity (Bryan et al., 2014; Challinor et al., 2014; Powell and Reinhard, 2016). In the context of smallholder agriculture, extreme weather events often exhibit significant localized negative impacts (Easterling et al., 2007). Beyond climate variabilities, many non-climatic, socio-economic and environmental stressors have

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also increased smallholder agriculture vulnerability (Morton, 2007). Sensitivity of smallholder agriculture to

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climate change has been acknowledged by the United Nations Sustainable Development Goals (SDGs) and

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urges adaptive action in smallholder agriculture to achieve food and nutrition security (HLPE, 2013). This entails finding ways to increase production while promoting sustainable and resilient agricultural practices.

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The impacts of climate variabilities on agricultural systems have been widely assessed (e.g. Bocchiola et al.,

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2019; Palazzoli et al., 2015) but the capacities of smallholders to confront, cope and thrive despite these changes are largely unmeasured. Understanding this resilience is essential for guiding agricultural policy

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decisions for smallholder agriculture.

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Emanating from ecology and describing a system's capacity to persist despite external perturbations (Holling, 1973), the concept of resilience embraces multiple dimensions, divergent perspectives, and definitions, in its evolution across disciplines and contexts (Hosseini et al., 2016). Despite these divergent perspectives, the general consensus is that resilience encompasses the absorptive, adaptive and transformative capacities of systems (Béné et al., 2012; Folke, 2016; Walker and Salt, 2006). While no such well-accepted and explicit definitions of resilience exist in the context of agroecosystems, the objective of measuring resilience in agriculture is to identify the capacities of farming systems to thrive despite perturbations and ensure its sustainability (O’Connell et al., 2015). We conceptualise smallholder resilience as the dynamic capacities of smallholder agriculture to maintain crop yields despite a variable climate, stemming from their endowment of socio-economic (e.g. education, wealth, management skill) and natural capital (e.g. soil, water), and their interactions.

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Journal Pre-proof As agro-ecological systems are complex and change over time, space and context, measuring resilience remains challenging (Darnhofer et al., 2010). Inductive, qualitative approaches are often used to capture the dynamic property of resilience in the form of case studies, qualitative interviews, and key informant consultation (Enfors and Gordon, 2007; Haider et al., 2012; Hammond et al., 2013). The qualitative approach generates rich, nuanced, and micro-level findings that are hard to generalise to infer policy implications (Malone and Rayner, 2001). A second approach to resilience research—indicator-based frameworks—has been widely used, but on an ad-hoc basis (Carpenter et al., 2005; Schwarz et al., 2011; Urruty et al., 2016). Quantification of indicators relevant to socio-economic, human, environmental, financial and institutional

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capitals in the sustainable livelihoods framework can capture a system’s capacity for adaptive response to

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adversity (Ifejika Speranza et al., 2014). However, Dixon and Stringer (2015) found that the indicator

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frameworks used in resilience assessment often fail to ground on the theoretical definition and so the selection of indicators are likely to explain resilience only in part. In a study of adaptive capacity of wheat production in

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Australia, Bryan et al. (2015) demonstrated that the theory-driven indicators often fail to correlate with

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resilience. Bryan et al. (2015) and Dixon and Stringer (2015) therefore warn to be circumspect in using the indicator-based approach without first empirically confirming the relevance of the selected indicators.

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Empirical approaches are therefore gaining attention in agricultural vulnerability and resilience research as they can overcome important caveats of mainstream methodologies (e.g. Antwi-Agyei et al., 2012; Mainali

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and Pricope, 2017; Meuwissen et al., 2019; Simelton et al., 2009). Quantifying the resilience of smallholder agriculture requires a broader framework that recognizes the socialecological complexities, location-specificities, climatic and non-climatic stressors, and crop physiological responses to climatic variabilities (Morton, 2007). Relevant works using empirical approaches are often framed around drought vulnerability. Keil et al. (2007) calculated an empirical drought resilience index for Indonesian farmers affected by the ENSO-related droughts as the reduction of expenditure for basic necessities, and demonstrated that the drought-affected farmers substantially reduced expenditures for food and basic necessities. Simelton et al. (2009) used a crop-yield based empirical approach to identify the resilient and sensitive regions for crop production using a drought vulnerability index, and used the findings to guide the selection of indicators. However, empirical assessments that do not include crop management

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Journal Pre-proof practices and the crop physiological responses to climate variability are likely to provide only limited insight into the resilience of food production systems (Bryan et al., 2014; Wang et al., 2019). In this study, we undertook an empirical assessment of the spatio-temporal patterns of resilience of smallholder agriculture across the regions of Far Western Provence, Nepal. We hypothesized that variation in actual cereal yields in regions with resilient food production systems would closely track that of climaticallydriven variation in expected yields. We used AquaCrop 6.1 (FAO, 2018), a process-based crop yield simulation model, to derive the expected yields of rice, wheat, and maize across all nine districts in the

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province for the 20 years from 1990 to 2010. Actual yields were quantified based on an observed crop yield dataset for the same period from the Ministry of Agricultural Development. We calculated resilience indices

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(RI) as the difference in yield anomalies of actual and expected yields for the respective crops. We then

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correlated RI against key socio-economic and natural capital variables to identify which variables may explain the resilience of smallholder agriculture in the bio-physically heterogeneous regions of Nepal. Finally, we

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discuss the implications of the results for reforming Nepal’s agricultural development policies, and for guiding

Methods

2.1 Study area

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further research on the resilience of smallholder cropping.

Supporting the livelihoods of the majority of the Nepalese people, agriculture accounts for around 30% of land in Nepal by area, contributes about one-third of the national gross domestic product (GDP), and employs 74% of the economically active population (CIAT et al., 2017). Nepal’s agriculture is characterised by smallholding practices as over 80% of farms are less than one hectare in area and production is primarily for familial subsistence (CBS, 2013). The Far Western Province—our study area—comprises nine districts, covers 19,539 km2, and is inhabited by almost 2.5 million people (CBS, 2016). The province encompasses 4,864 km2 of agricultural land (Figure 1a) under cultivation of crops often in rotation. Rice, wheat and maize are the major cereal crops (Figure 1b). The mountain districts are chronically food insecure, with moderate food insecurity in the Hill region (NeKSAP, 2014). The Far Western Province is among the poorest areas of the country (Sharma et al., 2014) and poverty has been pervasive for a long time (Zhang et al., 2018). In this -5-

Journal Pre-proof context, smallholder agriculture has been a major anchor for the subsistence and sustenance of people in the province (MoAD, 2017). The province comprises a range of climatic conditions spanning from tropical lowland in the south to snowcapped mountain with alpine climate in the north. Three physiographic regions—Terai, Hill, and Mountain— dominate the study area. The province experiences significant seasonal variability in temperature and precipitation due to the monsoonal climate (Figure 1c,d). Growing season rainfall and temperature varied in the study area (Figure S1). Irrigation, including seasonal irrigation, coverage is around 60% of the cropped

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area (CBS, 2013). Most irrigation systems are small-medium in size and are vulnerable to climate change due to their reliance on monsoon rainfall (Parajuli, 2017). The region is likely to observe frequent extreme weather

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events including prolonged drought, and lower and more erratic rainfall, as in other parts of the country

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(Karmacharya et al., 2007; Khatiwada et al., 2016; Wijngaard et al., 2017). These changes will have substantial implications for rainfed smallholder food production systems in the region of heterogeneities

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(Figure 2).

Figure 1 Study area physiography. a) Land use of the Far Western Province, Nepal (ICIMOD, 2013) with its geo-location in South Asia, b) Annual production of rice, wheat and maize in the study area districts averaged for the 20 year period from 1990-2010 (MoAD, 2014b), c) -6-

Journal Pre-proof and d) respectively the monthly mean temperature (°C) and monthly mean rainfall (mm) from 1990-2010 for the study area signifying the

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monthly variation in temperature and rainfall patterns (data from DoHM).

Figure 2 Examples of smallholder cropping in the study area a) High altitude smallholder regions in the Mountain districts experience snowfall during winter; b) and c) Crops are mostly cultivated in terraces in Hill and Mountain regions in both upland (b) and lowland (c) fields; d) Cattle manure, often mixed in litter, forms the primary source of fertilizer; e) Low-lying alluvial river terraces comprise the fertile land for crop production in the Hill and Mountain regions; f) The fertile tropical plains of the Terai region is suitable for the production of several varieties of crops.

2.2 Empirical measure of smallholder resilience The yields calculated using process-based crop yield simulation models represent the practically attainable crop yields (hereafter termed expected yield) for the given weather conditions and management. We -7-

Journal Pre-proof hypothesise that the districts that demonstrate actual crop yield anomalies equal to or above the expected yield anomalies characterise resilient smallholder food production systems. The approach assumes that the change in actual yield anomalies is the result of farmers' pro-activeness and responsiveness to climatic perturbations, underpinned by the capital variables. This approach enabled us to empirically quantify smallholder resilience. Correlation analysis helped to explain the possible role of the capital variables in determining resilience.

Crop yield simulations

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2.2.1

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We selected AquaCrop (version 6.1), a crop yield simulation model developed by the United Nation's Food and Agriculture Organisation (FAO), to estimate the expected crop yields. AquaCrop has been widely used

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and tested to simulate yields of herbaceous crops (e.g. Abdalhi et al., 2019; Ahmadi et al., 2015; Guo et al.,

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2019). We simulated the annual yields of rice, wheat and maize for each of the nine districts from 1990 to

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2010. AquaCrop simulates biomass production considering a soil-crop-atmosphere continuum, accounting for (i) plant growth, development and yield processes, (ii) temperature, rainfall, evapotranspiration and carbon

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dioxide concentrations, and (iii) soil and water balance (Steduto et al., 2009). AquaCrop also considers management aspects including fertilisation, irrigation and weed infestations in simulating yield as they affect

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crop development and productivity (Hsiao et al., 2011; Raes et al., 2009; Steduto et al., 2009). AquaCrop calculates biomass production ( ) based on the daily crop transpiration (Tr), reference evapotranspiration (ETo) and temperature stress coefficient (

) and the crop water productivity parameter (WP) (eq. 1). The

model derives actual herbaceous crop yield by multiplying the biomass production with the harvest index (HI) (eq. 2).

∑ (eq. 1)

(eq. 2) AquaCrop uses conservative and non-conservative parameters to simulate crop yields. The conservative parameters remain consistent under different growing conditions while the non-conservative parameters

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Journal Pre-proof depend upon the crop cultivar, management practices and the location-specific characteristics like soils and climate (Hsiao et al., 2011; Raes et al., 2009; Steduto et al., 2009). The conservative parameters considered in AquaCrop consists of the parameters that predict crop performance like temperature, water productivity, physiological parameters including stomatal closure and the soil-water depletion impacts on the crop. We used the default conservative parameters as they remain consistent for crops. The non-conservative parameters considered for simulations capture location-specific plant physiological development and yield development processes. We adopted non-conservative parameters for Terai region from Shrestha et al. (2013) to simulate

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crop yields (

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Journal Pre-proof Table 1). Non-conservative parameters for the Hill and Mountain districts were determined from previous experience and consultation with farmers, and also based on parameters previously used by Shrestha et al.

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(2013).

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Journal Pre-proof Table 1 Non-conservative and locally adjusted biophysical parameters used in crop modelling.

Non-conservative parameters

Rice

Wheat

Maize

Terai* Mountainǂ

Terai*

Mountainǂ

Terai*

Mountainǂ

38

38

160

160

9

9

Emergence

1

3

6

7

8

10

Max. canopy

91

114

50

61

61

77

Senescence

95

110

90

111

72

91

Maturity

117

150

122

150

95

120

Maximum canopy cover (%)

63

63

90

90

75

75

Flowering day (days from sowing)

80

102

66

66

61

77

Maximum root depth (m)

0.5

0.5

Average root zone expansion (m/day)

0.005

0.005

Reference harvest index (%)

41

41

Initial plant density (no. of plants per m2)

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1.0

1.0

0.005

0.005

0.012

0.012

38

32

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* Adapted from Shrestha et al. (2013),

Based on the researcher knowledge and consultations with farmers.

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Root deepening

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Canopy development (days from sowing)

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Irrigation and fertilisation rates vary significantly across the region. Time series data of fertiliser use across the districts were not available. Based on the lower amount of documented fertiliser use in the region (MoAD, 2017), we simulated crop yields in the Hill and Mountain under limited fertilisation rates (fertilisation at 50%) while the yields were simulated for the districts in the plains under moderate fertilisation based on consultation with the smallholder farmers during 2019 (see 2.2.3). Farmer consultation also indicated an irrigation interval of 7 and 10 days for rice grown in the lower plains and in the Hill/Mountain regions, respectively. Maize crops were considered to be not irrigated at all. Wheat yield was simulated under two irrigation regimes for the Terai districts whereas one irrigation was simulated for the Hill and Mountain districts. Daily meteorological data on temperature and rainfall from 15 stations were used for the crop yield simulations. Missing values in the station data were imputed from the AgMERRA Climate Forcing Dataset for Agricultural Modelling (Ruane et al., 2015). For imputation of missing values, the AgMERRA data better -11-

Journal Pre-proof captures the climate patterns and variability than using statistical derivation methods. Evapotranspiration was calculated using the FAO Penman-Monteith equation as it closely approximates grass ETo (Cai et al., 2007). Wind speed and solar radiation were extracted from the AgMERRA dataset as these variables were not recorded in the meteorological station data. We obtained soil data from the Harmonized World Soil Database v 1.2 (FAO et al., 2012) to calculate the soil properties required for the crop simulation model. The crop simulation model was run to estimate the expected yield across the nine districts of the study area for rice, wheat and maize with different crop calendars specified for the Mountain region and the Terai plains (Table 2).

Hill and Mountain

Rice

15 Jul – 14 Nov

1 Jul – 30 Nov

Wheat

15 Nov – 16 March

1 Nov – 30 March

Maize

15 April – 14 Jul

15 Mar – 15 Jul

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Table 2 Crop calendar used for yield simulation.

2.2.2 Deriving resilience indices

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We used actual crop yield data spanning the nine districts in our study area as reported by the Ministry of Agricultural Development (MoAD), Nepal, for the 20 years from 1990-2010 collected through crop-cut surveys (MoAD, 2014b). AquaCrop estimates provided the expected yield data. We used yield anomalies to capture the dynamics of the food production system (and thereby measure resilience), instead of using the absolute value of expected and actual yields to avoid systematic errors and bias from model calibration. We calculated yield anomaly indices normalised by the long-term mean of the yields for the crops and the districts as suggested by Bryan et al. (2015). Specifically, we calculated both the actual yield anomaly index and the expected yield anomaly index as the ratio of annual yield and yield averaged for the districts in all years as: ⁄

(

)

(eq. 3)

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Journal Pre-proof ⁄

Where each crop c.

and

(

)

(eq. 4)

are actual and expected normalised yield anomaly indices for district d and year y for and

are the actual and expected crop yields. Since both the yield anomaly indices

are normalised by the long-term averages, indices for each crop can be directly compared. We calculated the resilience index for each crop in each district for each year (

) in smallholder

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agriculture as the difference between the actual and the expected crop yield anomaly indices (eq. 5), where a higher value represents greater resilience to climatic variability. We then created of the overall resilience of as the average

(eq. 6).

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production share for each district and year

over the three crops weighted based on their relative

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smallholder cropping

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∑

(eq. 6) , and positive

values.

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Resilient smallholder agriculture would have

(eq. 5)

was calculated as the average of

for all years. We derived the resilience indices for the regions

combining the indices of each districts based on their production share. We categorised resilience for regions into low, medium and high using 30 and 70 percentile values of RI as the cut-off points to enable easier interpretation of the results.

2.2.3 Field verification We conducted onsite face-to-face interviews of 329 farmers across nine districts to acquire the data required for crop yield simulation from December 2018 to March 2019. Farmers were asked to reflect upon the crop management interventions they have used in the past. Stratified random sampling was used to identify interview participants. These consultations provided us with insights into smallholder farming practices in the study area and helped us adjust the management inputs for yield simulations, especially the irrigation

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Journal Pre-proof scheduling, fertilisation and the cropping calendar for the biophysically heterogeneous study area (Table 3). Ethics approval was acquired from Deakin University Human Ethics Advisory Group in November 2018. Table 3 Selected questions asked of farmers to capture crop management practices.



What schedule for sowing/planting, hoeing/weeding and harvesting do you practice for rice, wheat and maize crops?



What fraction of your farm used for the production of cereal crops has reliable irrigation facility? How frequently do you irrigate the different cereal crops? How much manure, fertilisers and pesticides do you usually apply in your farm for cereal production? How

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frequently do you apply fertilisers and pesticides?

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Climate variabilities during crop growth stages

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2.2.4 Determinants of resilience in smallholder cropping

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We derived climatic variables for the stages of crop growth and correlated them with RI to examine if climatic conditions of specific growth stages can explain variation in resilience. We used total rainfall, simple daily

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precipitation index, wet and dry days, mean temperature, diurnal temperature range, maximum temperature and number of days with temperature above 25°C (summer days) as these variables capture rainfall and

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temperature-related variabilities. The crop growth cycle was broadly divided into a) initial stage, b) canopy development stage, c) early growth stage and d) late-stage (Table S1), as used in AquaCrop. The climatic variabilities across the crop development stages were then correlated to the RI using Spearman’s rank correlation.

Capital indicators Resilience is implicit in agriculture-based livelihoods, and the sustainable livelihoods framework approach offers an objective measure to identify the factors that dictate resilience (Ifejika Speranza et al., 2014). We posit that the resilience of smallholder cropping is dictated by capital variables (natural, social, economic, financial and human) through a continual process of learning and reorganisation to enable the system to cope, buffer and thrive despite external perturbations. This aligns with previous work in resilience and adaptive -14-

Journal Pre-proof capacity research (e.g. Bryan et al., 2015; Ifejika Speranza, 2013; Keil et al., 2007; Rao et al., 2018). We selected 31 variables to characterise the capital indicators based on the availability of data for the Nepalese context (Table 4). We used non-parametric Spearman's rank correlation coefficients (rho) to quantify the strength and direction of the association between resilience and the capital variables as such relationships were likely to be monotonic.

Table 4 Capital indicators for smallholder resilience assessment

Indicators

Hypotheses

Source*

dependency

Ratio of the number of

The increase in dependency ratio would force smallholder

dependents with age < 14 and

farmers to adopt off-farm activities for subsistence and it leads

< 60 to the total population

to a negative relationship with resilience.

aged 15 to 60 Number of people in an area.

CBS

activities. A positive relationship is expected in labour intensive

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density

High density implies easy availability of labour for farm

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1.2 Population

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1.1 Age

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Social capital indicators

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1.

Definition

smallholder agriculture.

1.5 Population of

2.

The larger the household, the more labour available for

household.

agriculture which positively contributes to resilience.

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1.4 Sex ratio

Average number of persons in

The ratio of males to females

Gender-based roles in agriculture are better met with balanced

in a population.

household sex ratio, especially where male out-migration is

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1.3 Household size

CBS

CBS

predominant.

Percentage of Dalit and ethnic

The higher the share of dalit and ethnic population, the lower

dalit1 and ethnic

groups’ population in the total

the level of resilience as caste and ethnicity often limits the

groups

population.

access to information and services for adaptation.

CBS

Human capital indicators

2.1 Human

Composite measure of life

The farmers in the district with higher HDI would be in a

development

expectancy, education, and

position to learn and implement adaptive responses which is

index

per capita income.

expected to result in higher resilience.

Enable knowledge and skill

Literate farmers can access knowledge on adaptive practices, so

uptake.

a positive relationship with resilience is hypothesised.

2.2 Literacy rate

1

UNDP

CBS

Dalits are the community groups that are historically discriminated, exploited and excluded from mainstream socioeconomic and developmental process, and represent the tail-end untouchable caste groups in caste-based hierarchy in Hindu religion (Bennett, 2005; Kisan, 2009). -15-

Journal Pre-proof 2.3 Economically active

Percentage of people engaged

Economically active population injects investment and

in economic activity

innovation into agriculture. It is hypothesized to have a positive

population 2.4 Absentee population

CBS

relationship with resilience. Percent of people going

Absentee population means the loss of labour of age mostly

abroad for work to total

between 15-34 years from agriculture; a negative relationship

population as a labour

with resilience is expected.

CBS

migration. 2.5 Crop diversity

Diversity of acreage sown to

Higher values represent the capacity of farmers to maintain

different crops in the district

diversity to ensure resilience.

DoA

measured by Gini-Simpson

memberships

Capacity of farmers to

Higher membership rate infers the ability of farmers to access

operationalise saving-credit

financial resources to run farms.

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process in the village. Physical capital indicators

3.2 Road network density

Better irrigation coverage infers lower vulnerability to droughts

under irrigation.

and deficit rainfall; positive correlation expected.

Length of road per 100 sq. km.

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coverage

Percent of cultivated area

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3.1 Irrigation

Road network increases the accessibility to market for

DoA

MoRTP

agricultural inputs and outputs; positive relationship

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3.

MoAC

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2.6 Cooperative

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Index.

hypothesised.

Number of communication

Better access to extension services and information on farming

Extension

facilities (radio, TV, Mobile,

and climate change contributes to a higher level of resilience.

Services

& internet) per household.

3.4 Manure nitrogen to soil

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3.3 Access to

Amount of nitrogen added to

The greater the manure nitrogen input to soil, the more resilient

the farmland from the cattle

the food production system.

CBS

FAO

manure. 3.5 Landholding size

Size of farm held by the

The larger the landholding, the better the opportunities for

household.

agricultural mechanisation and diversification of crops. Bigger

MoAD

landholdings among smallholders is expected to contribute positively to resilience. 3.6 Mean slope of agriculture land

Averaged slope in degrees of

Higher slope infers higher rate of soil erosion, nutrient loss and

the cropping area

lower soil water retention, and is hypothesised to have a

STRM

negative correlation with resilience indices. 3.7 Land area cropped

Land area brought under

The larger the available area of cultivable farmland, the more

cultivation

opportunities for investment, diversification and mechanisation. -16-

DoA

Journal Pre-proof A positive relationship is expected with resilience. 4.

Natural capital indicators

4.1 Land cover diversity

4.2 Forest cover

Diversity of acreage of land

Higher land use diversity provides diverse ecosystem services.

use in the district measured by

Higher diversity is expected to contribute positively to

Gini-Simpson Index.

resilience.

Area under forest and

A greater proportion of forest and grassland infers a greater

grassland

availability of resources for farms (e.g. pollination) and

ICIMOD

ICIMOD

multiple ecosystem services. Forest cover is expected to contribute positively to resilience.

intensity

More rainfall ensures better water availability to crops; positive

year

relationship expected.

The average amount rain

Higher intensity of rainfall increases soil erosion and nutrient

averaged by the number of

loss, and is likely to result in a lower level of resilience.

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rainy days. Number of warm days (days

Higher number of warm days are likely to enhance yield in the

with max temp >= 25

Hill and Mountain regions resulting to higher resilience.

Mean of daily maximum

Higher maximum temperature is expected to aid crop growth in

DoHM

DoHM

DoHM

the study area, and is expected to positively correlate with

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temperature

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degrees) 4.6 Temperature

DoHM

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4.5 Warm days

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4.4 Precipitation

Total amount of rainfall in a

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4.3 Total rainfall

resilience.

matter content

Amount of carbon in soil

Higher soil organic matter content indicates the healthy soil,

which indicates the overall

and is expected to have a positive correlation with resilience.

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4.7 Soil organic

ISRIC

soil health 5.

Financial, economic and institutional capital indicators

5.1 Credit facilities

Number of commercial banks

The higher the number of banks nearby, the better access to

per district

credit to support farming; positive relationship with resilience

NRB

expected. 5.2 Civic engagement

Number of registered NGOs

Higher number is desirable as they work to enhance the

working in the district

capacity of farmers to cope, adapt and thrive under adversity

SWC

and a positive relationship with resilience is expected 5.3 Poverty rate

5.4 Proximity to market

The ratio of the number of

Higher poverty makes farmers unable to purchase improved

people whose income falls

seeds or agricultural inputs. A negative correlation is expected

below the poverty line.

with resilience.

Distance to the market

Distant markets infer farmers have lower access to agricultural inputs; a negative relationship is expected with resilience. -17-

CBS

CBS

Journal Pre-proof 5.5 Livestock number 5.6 Number of cooperative

The number of livestock

Higher values represent greater availability of organic fertiliser

owned by households.

for crops; a positive relationship is expected with resilience.

Number of cooperative groups

Higher number of cooperatives in the district provides farmers

in the district.

with easier access to financial resources and is hypothesised to

groups

DoA

NRB

have a positive correlation with resilience.

*Source: MoAC: Ministry of Agriculture and Cooperatives

DoA: Department of Agricultural Development

MoRTP: Mistry of Roads and Transport Planning

DoHM: Department of Hydrology and Meteorology

NRB: Nepal Rastra Bank

FAO: Food and Agriculture Organization

STRM: NASA's Shuttle Radar Topography Mission

ICIMOD: International Centre for Integrated Mountain Development

SWC: Social Welfare Council

ISRIC: Soil Reference and Information Centre

UNDP: United Nations Development Programme

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Results

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3.1 Yield anomaly and resilience indices

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CBS: Central Bureau of Statistics

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Spatial and temporal variability in yield anomaly indices was observed from 1990 to 2010 across the regions and crops assessed (Figure 3, Figure S2). EYIs were better tracked by the AYIs for rice in the Terai region

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and exhibited a strong positive correlation. Such close tracking of the anomaly indices was not evident in the

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northern regions with the Hill exhibiting a weakly positive correlation and a strongly negative relationship occurring in the Mountain regions. EYIs were less variable than the AYIs for rice in all regions. Both indices exhibited higher variability in the Hill (standard deviations for AY and EYI respectively were 0.22 and 0.15) while they were relatively smaller for the Mountain region. In the Terai, AYI variability for rice was highest (SD: 0.24) while the variability in EYI was the smallest (SD: 0.07). EYIs were better tracked by AYIs for maize and wheat in the Mountain region, exhibiting a stronger positive correlation. The tracking behaviour and the strength of the correlation between the anomaly indices was less pronounced in the Hill and substantially so in the Terai region. Variability in AYIs for both crops were higher than the EYIs in the Terai and Hill regions but EYIs were more variable in the Mountain region. Resilience indices (RI) also displayed substantial variation across the regions and crops over the years. RI exhibited cyclic runs of positive values for a few years followed by few years with negative RI values. The Terai region embodied the noticeable cyclic patterns of RI for all crops. Such cyclic patterns were distinct in wheat crops in all regions. RI were highly variable in the Terai region and in wheat. -18-

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Figure 3 Temporal pattern of crop yield anomaly and resilience indices. Actual and expected yield anomaly indices and resilience indices for rice, maize and wheat for regions for 1990-2010.

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Average annual RI from 1990-2010 for regions were small (-0.005 to 0.032, Table S2) but mostly positive for all crops. The Terai, Hill and Mountain regions manifested heightened levels of resilience respectively for wheat, rice and wheat, and maize (Figure 4). Regions with lower resilience in one crop often displayed a higher level of resilience in other crops. Lower resilience regions were characterised by their greater variability in RI. Aggregated resilience for regions after combining crop-specific RI revealed that no regions were resilient. The Hill and Mountain regions were moderately resilient in maintaining overall crop yields

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Wheat

Aggregated

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Maize

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Rice

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while the Terai region was least resilient.

Figure 4 Crop specific and aggregated resilience of smallholder agriculture for regions. The Terai region exhibited the lowest level of resilience among the regions.

3.2 Crop yield resilience and climate relationships 3.2.1 Rice -20-

Journal Pre-proof Diverse relationships were observed between climatic variables and RI across regions and crop growth stages (Figure 5). Both the Mountain and Terai regions often exhibited weak, negative correlations between total rainfall and the RI while the Hill had weak, positive relationships across all growth stages. Mid-season rainfall showed moderate, positive correlations for all crops. Correlations of other rainfall-related parameters (e.g. simple daily precipitation index and number of wet days) also often exhibited strong, positive and significant relationships with RI. None of the rainfall-related climatic variables for the growing season exhibited strong, significant relationships consistently across regions. Mean temperature showed mostly a positive correlation with Rice RI for all stages and regions. Mean temperature of canopy development stage exhibited a positive

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and strong correlation for all regions. The diurnal temperature range often had negative relationships with rice

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RI with stronger and few significant negative correlation coefficients for the Terai region. Maximum

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temperature exhibited a negative correlation with RI in the Terai and Mountain. Variables related to extreme temperatures (e.g. diurnal temperature range, maximum temperature, and number of summer days) often

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revealed relatively strong negative correlations with RI except for the Hill region across crop development

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stages.

Figure 5 Correlation between rice resilience indices and the climatic variables. Correlation coefficients calculated for rice crop growth stages and growing cycle during the period 1990-2010 by region.

3.2.2 Wheat Wheat exhibited mostly negative correlations with rainfall-related variables while the relationships were mostly positive with the temperature variables (Figure 6). Total rainfall, daily rainfall intensity, and the number of wet days during the mid-season and the growing cycle showed negative, strong and often significant correlation with the RI of wheat for all regions. Often the consistent relationships were observed

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Journal Pre-proof between RI and the crop growth stage rainfall-related variables for the regions. Temperature-related variables exhibited overall a positive correlation with RI, but significant relationships were observed mainly for the Mountain region. Relationships were moderately positive for the Hill while it was weak for the Terai region with few negative correlations (e.g. diurnal temperature range). Additionally, RI in Terai region exhibited

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negative correlations with temperature-related variables for the canopy development stage.

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crop growth stages during the period 1991-2010 by region.

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Figure 6 Correlation between wheat resilience indices and climatic variables. Correlation coefficients calculated for wheat growing cycle and

3.2.3 Maize

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The relationship of climatic variables and RI varied across regions and crop growth stages (Figure 7). The

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weakest correlations between RI and rainfall-related variables were observed for the Terai region while such relationships were positive, strong and often significant for the Hill and negative for the Mountain. Initial and late stages of crop growth showed strong and significant relationships between RI and the rainfall-related variables for the Hill and Mountain. Strong and significant correlations between RI and temperature-related variables were observed in the Mountain region, while such relations were mostly negative for Hill and mildly positive for the Terai. Correlations between temperature variables and the RI for crop development stages across the regions were inconsistent.

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Figure 7 Correlation between maize resilience indices and the climatic variables. Correlation coefficients calculated for maize growing cycle

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and crop growth stages during the period of 1990-2010 for regions.

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3.3 Crop yield resilience and capital indicator relationships

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Multiple indicators hypothesised to have a positive relationship with RI were found to be negatively correlated and vice versa (Figure 8). Social capital indicators exhibited mostly a weak correlation with RI. Population Density and

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Household Size were hypothesised to have positive correlations in labour intensive smallholder farming systems,

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but those indicators showed negative and weak correlation with RI. Most human capital indicators exhibited correlations as hypothesised except for Cooperative Membership. The Human Development Index and Literacy

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Rate had a moderate positive correlation with RI. For physical capital indicators, Road Access was expected to have a positive correlation with the resilience indices, but the analysis revealed that Road Access showed a strong

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negative correlation with RI. The Slope of Agricultural Land was expected to be negatively correlated with RI, but a weak and positive correlation was observed. Most natural capital indicators exhibited moderate to strong correlation with RI. Forest Cover, contrary to expectation, was found to have a moderately negative correlation. Financial and institutional capital indicators like the Number of Non-governmental Organisations (NGOs) working in the regions, Number of Cooperative Groups and Number of Financial Institutions were expected to have positive correlations with RI. The analysis demonstrated that these variables were negatively correlated with the RI. Poverty, on the other hand, the Rate and the Distance to Market were positively correlated, thus contradicting theory-based assumptions.

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Figure 8 Correlation between aggregated resilience indices and the capital indicators. Correlations of RI with capital indicators were mostly weak and often exhibited relationships contradicting theory-based assumptions.

4

Discussion

4.1 Resilience of smallholder cropping We have quantified the resilience of smallholder cropping to climatic variability in the biophysically heterogeneous Far Western Province, Nepal, for rice, maize and wheat. The methods used have provided a quantitative approach to climatic resilience assessment with limited datasets that could inform agricultural policy development in the region. Resilience was defined as the capacity of smallholder cropping to maintain crop yields despite climatic variability. We did not find very strong correlations between the actual and expected yield anomaly indices in any of the regions and districts. These differences in actual and expected yield anomalies informed the assessment of resilience of smallholder agriculture. Positive gaps between actual -24-

Journal Pre-proof and expected yield anomalies indicated resilience to climatic perturbations. The resilience indices were variable, with cyclic patterns observed in most districts following patterns in the crop growing season rainfall. Adaptive behavioural research suggests that the farmers may recognise climatic variabilities, and employ adaptation strategies and social innovations to respond to climatic risks (Thomas et al., 2007). Rahman et al. (2019) observed that the majority of farmers were late adopters of new approaches and practices. This suggests that there may be a time-lag between the recognition of perturbations and responses. The concept of the adaptive cycle by Gunderson (2001) also suggests that maintaining resilience is a time-lagged process – requiring time for farmers to learn and re-organise. Time-lagged exposure-response behaviour may explain

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the cyclic pattern of resilience found in this study.

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Rice is the preferred cereal crop and staple food in the region. Average resilience indices from 1990 to 2010

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suggested that the districts in the Hill region were resilient for rice yield. Despite having gently sloping land suitable for rice cropping, fertile soil, high irrigation coverage and mechanisation potential, the Terai region

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exhibited the weakest resilience for rice. Doti in the Hill region and Bajura in the Mountain region performed

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best in the resilience measure for rice. The Terai region was most resilient for wheat, and the Mountain region for maize. All of the regions maintained resilience for at least one crop. One potential explanation for this is

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that farmers’ adaptive strategies may have been to focus on one cereal crop in order to safeguard food

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security. Aggregated RI of the crops suggests that the Hill region was most resilient. In this region farmers used traditional farming methods, with few external inputs like seeds and fertilisers. In contrast, the Terai farmers where observed using new and more diverse seed varieties, and agrichemicals like herbicides and fertilisers. Nepal’s government aims to maximise crop production in this region due to its suitability for commercialisation, mechanisation, intensification and irrigation potential (MoAD, 2014a). However, our results suggest that the Terai underperformed in maintaining resilience. Informal interactions with farmers during fieldwork revealed that agricultural inputs were less likely to be available to farmers at the time of application. Though official statistics mentioned high coverage of irrigation in the Terai districts, the irrigation systems did not function well for their operational costs. For example, Shrestha and Dahal (2019) documented that bore-well irrigation systems were expensive. In interviews with farmers in Kanchanpur and Kailali districts, we were informed that farmers used bore-wells to irrigate rice only if a dry period persisted for over two weeks.

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Journal Pre-proof 4.2 Variables associated with resilience We correlated crop-specific resilience indices with climatic variables across crop development stages to explore the effect of climate on resilience. Positive, often significant, correlations were observed between RI and the mid-season rainfall variables. Rainfall variables were often negatively correlated in the Terai and Mountain regions as a higher amount and intensity of rainfall creates inundation and flooding in the Terai and excessive soil and nutrient loss in the Mountain, with detrimental impacts on rice yields. Maximum temperatures were also poorly and often negatively correlated in the Terai region. We observed higher episodes of maximum temperatures beyond the optimal threshold for rice production that were likely to the

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detriment of rice yields (Krishnan et al., 2011). While Bhatt et al. (2014) observed a strong correlation of rice

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yield with maximum temperature in high altitude regions, we did not observe such a relationship. Instead, the

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correlations were weak and mostly negative for the Mountain region. Water loss due to evapotranspiration with increasing temperature (Singh and Bengtsson, 2005) may have contributed to such a correlation. Unlike

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Asseng et al. (2011), we observed positive and often significant correlations between wheat RI and maximum

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temperature in the Mountain region. Such relationships were weaker in the Terai region and at times negative. Rather, wheat may have benefited from the increase in temperature as warmer temperatures in the cold, high

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elevation districts provided more suitable growing conditions. Tiwari and Yadav (2019) documented hightemperature stress on maize yield; whereas we observed a weaker correlation between maize resilience and

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temperature variables. Correlation coefficients suggest that some of the variables were better correlated to resilience than others, but in general climatic variables did not show a strong association with resilience for maize.

Under the sustainable livelihoods framework, we assessed the relationship of five capital variables of with resilience. Some of the variables that were expected to have positive correlation were negatively correlated with resilience and vice versa. Social capital variables exhibited weak correlations with RI. Population Density and the Household Size were expected to positively contribute to the resilience in predominantly labour-intensive smallholder agriculture as outmigration resulted in a shortage of labour availability (Jaquet et al., 2019) documented increased labour outmigration. Contrary to the hypothesis, these indicators exhibited a negative correlation with resilience. Higher population density may not have contributed to agricultural productivity as observed by Goldsmith et al. (2004) as our findings suggest that additional labour availability -26-

Journal Pre-proof did not stay actually increase productivity, resulting in a weak correlation. The proportion of dalit and ethnic groups in the total population exhibited a moderate negative correlation with resilience. This aligns with the findings of Jones and Boyd (2011) and Onta and Resurreccion (2011) that the restrictive social environment for ethnic and lower-caste groups (dalit) limit access to information and services, and adaptation measures. For the human capital indicators, Cooperative Memberships were hypothesised to have positive correlation with resilience as members of cooperatives could access the financial resources more easily than the nonmembers, but we found negative correlations. During fieldwork, we encountered many cases of off-farm use of credit from cooperatives as documented in Tiwari (2016), leading to farmers falling into debt. Off-farm

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investment of credit may have reduced dependence on agriculture leading to negative correlation between

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Cooperative Memberships and resilience.

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Physical capital indicators like Road Network Density were found to be strongly negatively correlated with smallholder resilience. Increase in road access can lead to a significant decrease in labour availability in

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agriculture (Asher and Novosad, 2018), via an increase in accessibility of off-farm work and results in the

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absence of the economically active population (UNFPA, 2017). As hypothesised, Landholding Size, Irrigation Coverage and the Access to Communication had a positive correlation with RI. Most natural capital indicators

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also exhibited strong positive correlations with resilience as hypothesised except for Forest Cover. Crop-

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raiding by wildlife in smallholder settings for increase in forest cover as observed by (Othman et al., 2005) may have contributed to the negative correlation of Forest Cover with yield resilience. Financial and institutional capital indicators were mostly negatively correlated with resilience. Access to Financial Institutions and Cooperatives were negatively correlated with resilience which may have resulted from the change in predominantly on-farm to predominantly off-farm livelihoods. Distance to Market was hypothesised to have a negative correlation with resilience as it will be difficult for farmers to access the market for agricultural inputs purchases and sales. Instead, we observed a positive correlation possibly due to the adoption of off-farm livelihoods.

4.3 Policy implications In regions where smallholder agriculture is a major source of livelihoods and food security, impacts of climate change on crop yield often result in heightened food insecurity along with effects on multiple other dimensions, including sustainability, health and wellbeing. Nepal is aiming to increase per capita food grain -27-

Journal Pre-proof production by 60% by 2030 as per its commitment to sustainable development goal (SDG) 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture in the country (NPC, 2017). Nepal’s Agricultural Development Strategy put forward the target of increasing total grain yield by 40% by 2035 (MoAD, 2016). With the effects of climate change becoming more evident in the region, achieving food security and promoting sustainable agriculture remains challenging. The Government of Nepal aims to maximise rice yield in the Terai region which we found to be the least resilient region for rice production. This results suggests that agricultural development policies in Nepal might be more effective with a focus on maintaining resilience rather than focusing solely on achieving yield growth. Nepal’s Agricultural

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Development Strategy 2015-2035 has identified activities to enhance agricultural productivity in the country.

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We found that some of the activities identified in the Agricultural Development Strategy such as crop

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diversification, promotion of adequately sized farms, communication and agricultural extension services, and increasing irrigation coverage had a positive correlation with resilience in the Far Western Province.

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Therefore, downscaling the strategy to provincial and local levels could capitalise on the variables found to be

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positively correlated with resilience in this study. Additionally, further scrutiny on the capital variables that resulted in negative correlation (e.g. market access, access to cooperatives and financial institutions and the

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presence of non-governmental organisations) is required. The availability of capitals like road access, market systems or natural resources are less likely to contribute to smallholder resilience as such unless the

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Agricultural Development Strategy promotes interventions to capitalise on them to add value to the smallholder agriculture. The negative correlation of resilience with caste and ethnicity based social diversity suggests that the Agricultural Development Strategy should focus on targeting developmental programs explicitly to those socially marginalized community groups. Agricultural development policy reformation requires the examination of capital variables to ensure theory-based assumptions do not misguide the policy intent and lead to policy failure.

4.4 Novelty and contribution We estimated the resilience of smallholder food production systems empirically using expected and actual yield anomalies. This approach captures the bio-physical heterogeneities via the use of a process-based crop yield simulation model to derive resilience using location-specific climatic, crop management, and -28-

Journal Pre-proof biophysical parameters. Unlike previous works, our approach to the quantification of resilience can be customised across spatial scales to capture local variabilities and is better suited for assessing the resilience of smallholder food production systems. Our finding on the relationships between capital indicators and resilience sheds a critical insight on the usability of theory-driven indicators as methods in resilience assessment. Most of the capital indicators had weak correlations and nearly half of the indicators contradicted the theoretical assumptions and hypotheses. It implies that several capital indicators failed to explain the resilience of smallholder agriculture in

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heterogeneous regions. This supports the use of data-driven empirical approaches to resilience assessment rather than theory-driven approaches. Additionally, our approach of empirical quantification of resilience

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enables researchers to test the suitability of the use of indicators for wider application.

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4.5 Uncertainties and limitations

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We used rainfall and temperature data from 15 meteorological stations in the province as input to our crop

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yield simulations. The meteorological stations lack data on solar radiation and wind speed that were required by the yield simulation model. We derived those parameters from the AgMERRA Climate Forcing Dataset for

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Agricultural Modelling (Ruane et al., 2015) despite the coarser resolution for our relatively small but biophysically heterogeneous study area. With sparse meteorological stations and coarse resolution of the

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available datasets, we could not capture important topo-climatic variations. Additionally, the quality of crop production data maintained by the Ministry of Agriculture was reported at the spatial scale of districts and did not account for yield variabilities for different crop management including irrigation and soil. District level production data was at the finest resolution available for Nepal. While data of the same resolution has been used to establish climate-crop relationships in previous studies (e.g. Bhatt et al. (2014); and Palazzoli et al. (2015)), there is some uncertainty resulting from the resolution and level of aggregation of the input data. Crop management has a significant impact on crop yields (Bryan et al., 2014) and annual variability in yield resulting from variation in management interventions was not captured in the assessment. However, through our field survey we were able to ascertain general trends and patterns of realistic crop management practices which partially overcame this source of uncertainty. We did not validate the results of crop yield simulation models with the field experimental data for regions with micro-climatic variations as this was beyond the scope of this study. Avoid systematic error and bias in the absolute values of yield generated by simulation -29-

Journal Pre-proof models in our assessments by focusing instead on the relative change (i.e. anomalies) in yield. We often observed weak relationships between resilience and climatic parameters and capital indicators. Capturing topo-climatic and management variabilities could enhance such correlations. We limited our study to the assessment of yield resilience with the assumption that the yield is attributed by the smallholder farming system’s absorptive, adaptive and transformative capacities without explicitly quantifying those capacities. Quantification of such capacities in bio-physically heterogeneous regions could provide further insights on the determinants of resilience.

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4.6 Conclusion Our empirical quantification of resilience provides new insights into the capacities of smallholder agriculture

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to maintain yields despite climatic variability for major cereal crops in the Far Western Province of Nepal

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from 1990 to 2010. Smallholders in the Terai, Hill and Mountain regions were found to be resilient in wheat,

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wheat and rice, and maize production, respectively. The Hill and Mountain regions maintained a moderate level of overall resilience in cereal production, while the highest variability in resilience was observed for

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wheat in the Terai region. The relationship between climate and resilience varied across crop development stages and differed between regions. Rainfall in the mid-season of rice growth positively contributed to

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resilience for all regions. The higher temperature was mostly detrimental to the resilience of rice in both the

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Terai and Mountain regions, while an increase in temperature was beneficial for wheat in the Mountain region. Our results demonstrated that correlations with RI for 14 out of 31 capital indicators selected under the sustainable livelihoods framework contradicted theory-driven assumptions, and most of the indicators were weakly related to resilience. These findings suggest that capital variables may often fail to explain the resilience of smallholder agriculture. We conclude that explorative fine‑ scale social research is essential to assist with interpretation of empirical assessment in understanding the predictors of the resilience of smallholder agriculture in heterogeneous regions. Agricultural development policies could align with the capital variables that demonstrated a positive correlation with the resilience rather than ad-hoc adoption of theory-driven assumptions which could misguide agricultural policy development.

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Journal Pre-proof Conflict of interest: none Acknowledgements: This study was financially supported by Deakin University, Australia. Authors would like to thank farmers involved in interviews, governmental authorities that supported the fieldwork, and the

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Department of Hydrology and Meteorology (DHM), Nepal for meteorological data.

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Journal Pre-proof References: Abdalhi, MAM, Jia, Z, Luo, W, Tang, S, Ali, OO, Cheng, J. (2019). FAO AquaCrop Model Performance: in Green Canopy Cover, Soil Moisture and Production of Maize at Middle and Lower Reaches Plain of Yangtze River of China. Russian Agricultural Sciences, 45(2), 186-193. Ahmadi, SH, Mosallaeepour, E, Kamgar-Haghighi, AA, Sepaskhah, AR. (2015). Modeling Maize Yield and Soil Water Content with AquaCrop Under Full and Deficit Irrigation Managements. Water Resources Management, 29(8), 2837-2853. Antwi-Agyei, P, Fraser, EDG, Dougill, AJ, Stringer, LC, Simelton, E. (2012). Mapping the vulnerability of crop production to drought in Ghana using rainfall, yield and socioeconomic data. Applied Geography, 32(2), 324-334.

of

Asher, S, Novosad, P. (2018). Rural Roads and Local Economic Development: The World Bank. Asseng, S, Foster, I, Turner, NC. (2011). The impact of temperature variability on wheat yields. Global Change Biology,

ro

17(2), 997-1012.

-p

Béné, C, Wood, RG, Newsham, A, Davies, M. (2012). Resilience: New Utopia or New Tyranny? Reflection about the Potentials and Limits of the Concept of Resilience in Relation to Vulnerability Reduction Programmes. IDS

re

Working Papers, 1-61.

Bennett, L. (2005). Gender, caste and ethnic exclusion in Nepal: Following the policy process from analysis to action.

lP

Washington, DC: World Bank (http://siteresources. worldbank. org/INTRANETSOCIALDEVELOPMENT/Resources/Bennett. rev. pdf).

na

Bhatt, D, Maskey, S, Babel, MS, Uhlenbrook, S, Prasad, KC. (2014). Climate trends and impacts on crop production in the Koshi River basin of Nepal. Regional Environmental Change, 14(4), 1291-1301.

Jo ur

Bocchiola, D, Brunetti, L, Soncini, A, Polinelli, F, Gianinetto, M. (2019). Impact of climate change on agricultural productivity and food security in the Himalayas: A case study in Nepal. Agricultural Systems, 171, 113-125. Brown, PR, Afroz, S, Chialue, L, Chiranjeevi, T, El, S, Grünbühel, CM, Khan, I, Pitkin, C, Reddy, VR, Roth, CH, Sacklokham, S, Williams, LJ. (2019). Constraints to the capacity of smallholder farming households to adapt to climate change in South and Southeast Asia. Climate and Development, 11(5), 383-400. Bryan, BA, Huai, J, Connor, J, Gao, L, King, D, Kandulu, J, Zhao, G. (2015). What actually confers adaptive capacity? Insights from agro-climatic vulnerability of Australian wheat. PLoS One, 10(2). Bryan, BA, King, D, Zhao, G. (2014). Influence of management and environment on Australian wheat: information for sustainable intensification and closing yield gaps. Environmental Research Letters, 9(4). Cai, J, Liu, Y, Lei, T, Pereira, LS. (2007). Estimating reference evapotranspiration with the FAO Penman–Monteith equation using daily weather forecast messages. Agricultural and Forest Meteorology, 145(1), 22-35. Carpenter, SR, Westley, F, Turner, MG. (2005). Surrogates for Resilience of Social–Ecological Systems. Ecosystems, 8(8), 941-944. CBS. (2013). National Sample Census of Agriculture, Nepal 2011/12 National Report. Kathmandu: Central Bureau of Statistics, Nepal. -32-

Journal Pre-proof CBS. (2016). Statistical Pocket Book of Nepal. 2017: Central Bureau of Statistics, Government of Nepal. Challinor, AJ, Watson, J, Lobell, DB, Howden, SM, Smith, DR, Chhetri, N. (2014). A meta-analysis of crop yield under climate change and adaptation. Nature Climate Change, 4, 287. CIAT, World Bank, CCAFS, LI-BIRD. (2017). Climate-Smart Agriculture in Nepal. CSA Country Profiles for Asia Series. (pp. 26). Washington, D.C.: International Center for Tropical Agriculture (CIAT); The World Bank; CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS); Local Initiatives for Biodiversity Research and Development (LI-BIRD). Darnhofer, I, Fairweather, J, Moller, H. (2010). Assessing a farm's sustainability: insights from resilience thinking. International Journal of Agricultural Sustainability, 8(3), 186-198. Dixon, JL, Stringer, LC. (2015). Towards a Theoretical Grounding of Climate Resilience Assessments for Smallholder

of

Farming Systems in Sub-Saharan Africa. Resources-Basel, 4(1), 128-154.

ro

Easterling, WE, Aggarwal, PK, Batima, P, Brander, KM, Erda, L, Howden, M, Kirilenko, A, Morton, JF, Soussana, J-F, Schmidhuber, J, Tubiello, F. (2007). Food, fibre and forest products. In M. L. Parry, O. F. Canziani, J. P.

-p

Palutikof, et al. (Eds.), Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working

Cambridge, UK: Cambridge University Press.

re

Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, (pp. 273-313).

lP

Eissler, S, Thiede, BC, Strube, J. (2019). Climatic variability and changing reproductive goals in Sub-Saharan Africa. Global Environmental Change, 57, 101912.

Enfors, EI, Gordon, LJ. (2007). Analysing resilience in dryland agro-ecosystems: a case study of the Makanya catchment

FAO. (2018). AquaCrop Version 6.1.

na

in Tanzania over the past 50 years. Land Degradation & Development, 18(6), 680-696.

Jo ur

FAO, IIASA, ISRIC, ISSCAS, JRC. (2012). Harmonized world soil database (version 1.2). from FAO, Rome, Italy and IIASA, Laxenburg, Austria

Folke, C. (2016). Resilience (Republished). Ecology and Society, 21(4). Goldsmith, PD, Gunjal, K, Ndarishikanye, B. (2004). Rural–urban migration and agricultural productivity: the case of Senegal. Agricultural Economics, 31(1), 33-45. Gunderson, LH. (2001). Panarchy: understanding transformations in human and natural systems: Island press. Guo, Y, Wu, W, Du, M, Bryant, CR, Li, Y, Wang, Y, Huang, H. (2019). Assessing Potential Climate Change Impacts and Adaptive Measures on Rice Yields: The Case of Zhejiang Province in China. Sustainability, 11(8), 2372. Haider, LJ, Quinlan, AE, Peterson, GD. (2012). Interacting traps: resilience assessment of a pasture management system in northern Afghanistan. Planning theory and practice, 13(2), 312-319. Hammond, B, Berardi, G, Green, R. (2013). Resilience in Agriculture: Small- and Medium-Sized Farms in Northwest Washington State. Agroecology and Sustainable Food Systems, 37(3), 316-339. HLPE. (2013). Investing in smallholder agriculture for food security. A report by the High Level Panel of Experts on Food Security and Nutrition of the Committee on World Food Security. Rome: UN Committee on World Food Security. -33-

Journal Pre-proof Holling, CS. (1973). Resilience and Stability of Ecological Systems. Annual review of ecology and systematics, 4(1), 1-23. Hosseini, S, Barker, K, Ramirez-Marquez, JE. (2016). A review of definitions and measures of system resilience. Reliability Engineering & System Safety, 145, 47-61. Hsiao, TC, Fereres, E, Steduto, P, Raes, D. (2011). AquaCrop parameterization, calibration, and validation guide, Crop yield response to water. Irrig. Drain. Pap, 66, 70-87. Hussain, A, Mahapatra, B, Rasul, G. (2019). Adaptation in Mountain Agriculture: Food Security in the Hindu-Kush Himalayan (HKH) Region. In M. Alam, J. Lee & P. Sawhney (Eds.), Status of Climate Change Adaptation in Asia and the Pacific (pp. 211-236). Cham: Springer International Publishing. ICIMOD. (2013). Land cover of Nepal 2010.

agriculture. Regional Environmental Change, 13(3), 521-535.

of

Ifejika Speranza, C. (2013). Buffer capacity: capturing a dimension of resilience to climate change in African smallholder

ro

Ifejika Speranza, C, Wiesmann, U, Rist, S. (2014). An indicator framework for assessing livelihood resilience in the context of social–ecological dynamics. Global Environmental Change, 28(Supplement C), 109-119.

Management Strategies. Sustainability, 11(5), 1349.

-p

Jaquet, S, Kohler, T, Schwilch, G. (2019). Labour Migration in the Middle Hills of Nepal: Consequences on Land

re

Jones, L, Boyd, E. (2011). Exploring social barriers to adaptation: Insights from Western Nepal. Global Environmental

lP

Change, 21(4), 1262-1274.

Karmacharya, J, Shrestha, A, Rajbhandari, R, Shrestha, M. (2007). Climate change scenarios for Nepal based on regional climate model RegCM3 (pp. 35). Kathmandu: Department of Hydrology and Meteorology.

na

Keil, A, Zeller, M, Wida, A, Sanim, B, Birner, R. (2007). What determines farmers’ resilience towards ENSO-related drought? An empirical assessment in Central Sulawesi, Indonesia. Climatic Change, 86(3), 291.

Jo ur

Khatiwada, K, Panthi, J, Shrestha, M, Nepal, S. (2016). Hydro-Climatic Variability in the Karnali River Basin of Nepal Himalaya. Climate, 4(2), 17.

Kisan, YB. (2009). A Study of Dalits' Inclusion in Nepali State Governance. Identity and Society: Social Exclusion and Inclusion in Nepal, 45-65.

Krishnan, P, Ramakrishnan, B, Reddy, KR, Reddy, VR. (2011). High-Temperature Effects on Rice Growth, Yield, and Grain Quality. In D. L. Sparks (Ed.), Advances in Agronomy (Vol. 111, pp. 87-206): Academic Press. Lobell, DB, Cahill, KN, Field, CB. (2007). Historical effects of temperature and precipitation on California crop yields. Climatic Change, 81(2), 187-203. Lowder, SK, Skoet, J, Raney, T. (2016). The Number, Size, and Distribution of Farms, Smallholder Farms, and Family Farms Worldwide. World Development, 87, 16-29. Mainali, J, Pricope, NG. (2017). High-resolution spatial assessment of population vulnerability to climate change in Nepal. Applied Geography, 82, 66-82. Malone, E, L. , Rayner, S. (2001). Role of the research standpoint in integrating global-scale and local-scale research. Climate Research, 19(2), 173-178. -34-

Journal Pre-proof Meuwissen, MPM, Feindt, PH, Spiegel, A, Termeer, CJAM, Mathijs, E, Mey, Yd, Finger, R, Balmann, A, Wauters, E, Urquhart, J, Vigani, M, Zawalińska, K, Herrera, H, Nicholas-Davies, P, Hansson, H, Paas, W, Slijper, T, Coopmans, I, Vroege, W, Ciechomska, A, Accatino, F, Kopainsky, B, Poortvliet, PM, Candel, JJL, Maye, D, Severini, S, Senni, S, Soriano, B, Lagerkvist, C-J, Peneva, M, Gavrilescu, C, Reidsma, P. (2019). A framework to assess the resilience of farming systems. Agricultural Systems, 176, 102656. MoAD. (2014a). Agriculture Development Strategy (ADS). Kathmandu: Ministry of Agricultural Development. MoAD. (2014b). District level cereal crop production in Nepal. MoAD. (2016). Agriculture Development Strategy (ADS) 2015 to 2035. Kathmandu, Nepal: Ministry of Agricultural Development. MoAD. (2017). Statistical Information on Nepalese Agriculture. Kathmandu: Ministry of Agricultural Development.

of

Morton, JF. (2007). The impact of climate change on smallholder and subsistence agriculture. Proceedings of the National

ro

Academy of Sciences, 104(50), 19680-19685.

NeKSAP. (2014). Food Security Information System for Nepal. Retrieved Aug 18, 2019, from

-p

http://geoapps.icimod.org/npfoodsecurity

NPC. (2017). Nepal’s Sustainable Development Goals Status and Roadmap: 2016-2030. Kathmandu Nepal: National

re

Planning Commission.

lP

O’Connell, D, Walker, B, Abel, N, Grigg, N. (2015). The resilience, adaptation and transformation assessment framework: from theory to application. Australia: CSIRO.

Onta, N, Resurreccion, BP. (2011). The Role of Gender and Caste in Climate Adaptation Strategies in Nepal. Mountain

na

Research and Development, 31(4), 351-356, 356.

Othman, WJ, Jiddawi, N, Bryceson, I. (2005). Social-ecological resilience of Pete and Maruhubi mangrove ecosystems in

Jo ur

Zanzibar, Tanzania. Marine Protected Areas (MPAs), 125. Palazzoli, I, Maskey, S, Uhlenbrook, S, Nana, E, Bocchiola, D. (2015). Impact of prospective climate change on water resources and crop yields in the Indrawati basin, Nepal. Agricultural Systems, 133(Supplement C), 143-157. Parajuli, UN. (2017). Impact of Climate Change on Small and Medium Irrigation Systems in Nepal. Powell, JP, Reinhard, S. (2016). Measuring the effects of extreme weather events on yields. Weather and Climate Extremes, 12, 69-79. Raes, D, Steduto, P, Hsiao, TC, Fereres, E. (2009). AquaCrop—The FAO Crop Model to Simulate Yield Response to Water: II. Main Algorithms and Software Description. Agronomy Journal, 101(3), 438-447. Rahman, MA, Islam, R, Falguni, MR, Shahriar, SA, Rahman, MA, Rahman, MM. (2019). Adoption of BRRI dhan49 by the Farmers of Bogura Sadar Upazila in Bangladesh. Asian Journal of Research in Agriculture and Forestry, 1-8. Rao, CS, Kareemulla, K, Krishnan, P, Murthy, GRK, Ramesh, P, Ananthan, PS, Joshi, PK. (2018). Agro-ecosystem based sustainability indicators for climate resilient agriculture in India: A conceptual framework. Ecological Indicators.

-35-

Journal Pre-proof Ruane, AC, Goldberg, R, Chryssanthacopoulos, J. (2015). Climate forcing datasets for agricultural modeling: Merged products for gap-filling and historical climate series estimation. Agricultural and Forest Meteorology, 200, 233248. Samberg, L, H. , Gerber, J, S. , Ramankutty, N, Herrero, M, West, P, C. (2016). Subnational distribution of average farm size and smallholder contributions to global food production. Environmental Research Letters, 11(12), 124010. Schwarz, A-M, Béné, C, Bennett, G, Boso, D, Hilly, Z, Paul, C, Posala, R, Sibiti, S, Andrew, N. (2011). Vulnerability and resilience of remote rural communities to shocks and global changes: Empirical analysis from Solomon Islands. Global Environmental Change, 21(3), 1128-1140. Sharma, P, Guha-Khasnobis, B, Khanal, DR. (2014). Nepal Human Development Report 2014: Beyond Geography Unlocking Human Potential. Kathmandu: National Planning Commission and United Nations Development

of

Programme.

ro

Shrestha, N, Raes, D, Sah, SK. (2013). Strategies to Improve Cereal Production in the Terai Region (Nepal) during Dry Season: Simulations With Aquacrop. Procedia Environmental Sciences, 19, 767-775.

-p

Shrestha, S, Dahal, KR. (2019). Comparison of Operational Cost between Deep Tubewell Irrigation and Borewell Irrigation System in Parasan, Kanchanpur District, Nepal. American Scientific Research Journal for Engineering,

re

Technology, and Sciences (ASRJETS), 52(1), 21-27.

lP

Simelton, E, Fraser, EDG, Termansen, M, Forster, PM, Dougill, AJ. (2009). Typologies of crop-drought vulnerability: an empirical analysis of the socio-economic factors that influence the sensitivity and resilience to drought of three major food crops in China (1961–2001). Environmental Science & Policy, 12(4), 438-452.

na

Singh, P, Bengtsson, L. (2005). Impact of warmer climate on melt and evaporation for the rainfed, snowfed and glacierfed basins in the Himalayan region. Journal of Hydrology, 300(1), 140-154.

Jo ur

Steduto, P, Hsiao, TC, Raes, D, Fereres, E. (2009). AquaCrop—The FAO Crop Model to Simulate Yield Response to Water: I. Concepts and Underlying Principles. Agronomy Journal, 101(3), 426-437. Thomas, DSG, Twyman, C, Osbahr, H, Hewitson, B. (2007). Adaptation to climate change and variability: farmer responses to intra-seasonal precipitation trends in South Africa. Climatic Change, 83(3), 301-322. Tiwari, L. (2016). Financial Status of Small Farmer Cooperative Limited (SFCL) in Surkhet District of Nepal. Journal of Advanced Academic Research, 3(1), 37-50. Tiwari, YK, Yadav, SK. (2019). High Temperature Stress Tolerance in Maize (Zea mays L.): Physiological and Molecular Mechanisms. Journal of Plant Biology, 62(2), 93-102. UNFPA. (2017). Population Situation Analysis of Nepal. Kathmandu: United Nations Population Fund. Urruty, N, Tailliez-Lefebvre, D, Huyghe, C. (2016). Stability, robustness, vulnerability and resilience of agricultural systems. A review. Agronomy for Sustainable Development, 36(1), 15. Waddington, SR, Li, X, Dixon, J, Hyman, G, de Vicente, MC. (2010). Getting the focus right: production constraints for six major food crops in Asian and African farming systems. Food Security, 2(1), 27-48. Walker, B, Salt, D. (2006). Resilience thinking: sustaining ecosystems and people in a changing world: Island Press. -36-

Journal Pre-proof Wang, E, Brown, HE, Rebetzke, GJ, Zhao, Z, Zheng, B, Chapman, SC. (2019). Improving process-based crop models to better capture genotype×environment×management interactions. Journal of Experimental Botany, 70(9), 23892401. West, JJ, Haug, R. (2017). The vulnerability and resilience of smallholder-inclusive agricultural investments in Tanzania. Journal of Eastern African Studies, 11(4), 670-691. Wijngaard, RR, Lutz, AF, Nepal, S, Khanal, S, Pradhananga, S, Shrestha, AB, Immerzeel, WW. (2017). Future changes in hydro-climatic extremes in the Upper Indus, Ganges, and Brahmaputra River basins. PLoS One, 12(12), e0190224. Zhang, J, Liu, C, Hutton, C, Koirala, HL. (2018). Geographical Dynamics of Poverty in Nepal between 2005 and 2011:

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Where and How? Sustainability, 10(6), 2055.

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CRediT author statement

Prahlad Lamichhane: Conceptualization, Methodology, Analysis, Visualization, Writing Kelly K Miller: Conceptualization, Methodology, Review and Editing Michalis Hadjikakou: Conceptualization, Methodology, Review and Editing

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Brett A Bryan: Conceptualization, Methodology, Review and Editing

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Journal Pre-proof Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

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Prahlad Lamichhane

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On behalf of authors,

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Graphical abstract

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Journal Pre-proof Highlights

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Smallholder system resilience was quantified for socio-ecologically diverse regions. Smallholder resilience varied across regions and crops. Capital indicators showed weak and often contradictory relation with resilience. Results can be used to inform agricultural policy decisions including SDGs Detailed social research is needed to further explain drivers of resilience.

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