Open Access

The challenge of achieving basal energy, iron and zinc provision for home consumption through family farming in the Andes: a comparison of coverage through contemporary production systems and selected agricultural interventions

Agriculture & Food Security20165:23

https://doi.org/10.1186/s40066-016-0071-7

Received: 12 February 2016

Accepted: 1 October 2016

Published: 15 October 2016

Abstract

Background

Child undernutrition is persistently high in the central Andes of Peru, and numerous smallholder households fail to meet their basic needs of energy, iron and zinc. Food-based approaches assume household-level nutrition can be improved following agricultural interventions. This study assesses for the first time whether current Andean production systems provide sufficient energy, iron and zinc output to meet household-level requirements and explores the likely effect of commonly promoted food-based approaches. Across four communities, we determined the crop and livestock production output for each household (n = 165) during one growing season. The household-level nutritional demand or input was calculated as a function of household composition and daily requirements of energy, iron and zinc as established by FAO/WHO. We examined five scenarios, current practice or status quo and four food-based interventions: (1) increased potato yield, (2) introduced biofortified potatoes, (3) promotion of guinea pigs and (4) a mixed strategy combining all of the above.

Results

Under status quo, 86, 62 and 76 % of households obtained sufficient production output to meet energy, iron and zinc requirements, respectively. Considering the three parameters simultaneously, 59 % of households were able to meet their energy, iron and zinc requirements. The total crop production among households provided more than the necessary energy, iron and zinc output to meet the demand of all 165 households. Yet, significant differences between households account for individual deficits or surpluses in household-level output–input balances. Potato (Solanum spp.), barley (Hordeum vulgare) and faba (Vicia faba) production was particularly significant in determining the energy, iron and zinc output. Livestock did not make a substantial contribution. The main difference between households with negative versus positive coverage, in terms of household-level production output from agriculture meeting demand (=input), was available cropping area given household size. None of the explored food-based interventions closed the energy, iron and zinc deficit from production among households with negative coverage.

Conclusions

The smallholder production systems analyzed are only partially capable of providing sufficient production output to cover household-level energy, iron and zinc demands. Of the four interventions examined, a mixed strategy holds most potential for reducing nutrition gaps. Particularly potato yield increases had a positive effect. The carrying capacity of high-altitude Andean farming systems is strained for households with limited land. Food-based approaches to nutrition under scenarios similar to those reported in this study are advised to balance agricultural interventions with options to enhance off-farm access to food.

Keywords

Food and nutrition security Smallholder farmers Food-based approaches Central Andes Peru

Background

Agriculture is a vital source of food for farm households in the central Andes of Peru and a main entry point for development interventions aimed at strengthening food security and reducing undernutrition. Food-based approaches to nutrition, particularly those involving enhanced on-farm production to increase yields or to incorporate micronutrient dense components such as microlivestock (e.g., guinea pigs, chickens), biofortified staples or vegetables in smallholder farming systems, are frequently considered to be robust and sustainable alternatives to enhance smallholder food security [1, 2]. Higher or more stable crop yields are expected to influence the availability dimensions of food security, while diet quality from micronutrient rich foods can directly affect food security through utilization. Importantly, enhanced on-farm production as a food-based approach to improve nutrition is considered sustainable. In contrast to non-food-based approaches where nutritional supplementation, for example, must be provided in an ongoing way by agents external to the community, food-based alternatives can be autonomously maintained by farmers.

One underexplored prerequisite for the success of food-based approaches relates to the actual capacity of the smallholder farming system to provide sufficient food or nutrients to cover basic household-level nutritional requirements. Empirical assessments of these crop–livestock systems’ energy and nutrient outputs are needed. In addition, analyses of the actual contribution of selected agricultural (food-based) interventions to household-level nutrient availability are critical to fully understand their potential impact.

Undernutrition is still prevalent in the Peruvian Andes, disproportionately affecting rural areas [3]. Stunting, or low height for age, is particularly widespread and has been the main focus of attention for governmental and civil society nutrition interventions in Peru. Stunting reflects the cumulative effects of undernutrition and poor health [4]. It is an indication of poor food and/or environmental health and results in long-term restrictions on child growth potential. Between 2000 and 2011, stunting in Peruvian children younger than 5 years of age went down from 31.6 to 19.6 %; anemia dropped from 50.4 to 30.7 % [5]. For the same period, stunting rates in Peru’s highland regions dropped from 43.5 to 30.8 %; anemia dropped from 56.2 to 39.9 %. Despite overall progress, the prevalence of anemia in children 6–36 months old has remained disproportionately high with 41.6 % at the national level and 49.6 % in rural areas in 2011 [6]. By 2013, these figures actually increased to 46.4 % nationwide and 51.7 % in rural areas.

The reduction in child malnutrition in Peru is the result of increasingly coordinated government and civil society actions, e.g., a national poverty reduction strategy prioritizing nutrition interventions [7, 8]. Nutrition programs in Peru have focused on improving food access and availability. A conditional cash transfer program (Juntos) for women and food supplementation through the “glass of milk” and “school breakfast” programs have been at the heart of governmental strategies for the last decade [810]. Fortification of commonly consumed products, such as wheat flour, noodles or prepackaged school breakfast meals, is also regularly applied in Peru [11, 12], but these do not target infant and young children under 3 years old—the most vulnerable group for nutritional deficiencies and their child development consequences. On the other hand, the strategy of multi-micronutrient powder or sprinkles for children 6–36 months has been applied irregularly by the Peruvian government. In addition, many initiatives have addressed food utilization practices, e.g., improved hygiene and sanitation through nutrition messaging [13].

Meanwhile, agricultural or food-based approaches in the Andes have been predominantly promoted by civil society organizations. Interventions include the promotion of small greenhouses, horticulture, microlivestock, crop diversification and to a lesser extent biofortified crops [14]. In parallel, international development programs have focused their attention on increasing farmer income through inclusive value chain initiatives [15, 16]. Food stability is probably the food security dimension that is least attended by institutional interventions. Yet, Andean practices such as field scattering, mixed crop–livestock portfolios, planting of varietal combinations are still widely used by farmers themselves to maximize stability [1719].

Research and development organizations frequently assume that agricultural (food-based) interventions will effectively translate into significant household-level nutrition improvements. Different types of social, economic or biophysical factors can impede desired outcomes. On-farm production is taking place in the high Andes as smallholder farmers rapidly diversify their livelihoods through off-farm employment [2023]. Male farmers are increasingly only part-time involved in agriculture, and as women stay behind, a feminization of agriculture is becoming a reality in many rural areas [2427]. Moreover, while in some places (temporal) migration has released some of the pressure on the land’s ability to sustain food production [28], in others continued demographic pressure and landholding fragmentation reinforce migration and intensify crop and livestock production [2931].

Particularly where smallholder farming systems may face constraints to production, it is imperative to assess actual carrying capacity and the potential of currently promoted agricultural interventions to enhance it. Carrying capacity defines the balance between land resources and human demands [3240]. Locally, for smallholder food security, it translates into the total annual crop–livestock output being able to cover household demand for food. Sustainable intensification, it is argued, can extend the carrying capacity of the land to meet human food security needs [4143]. In Andean agricultural systems, intensification options include the promotion of new varieties, crop biodiversity, homegardens or better practices for soil fertility, handling pests and disease, and storage of target crops, among others [4451]. The desired outcome of such efforts is to increase the household-level food and nutrient quantity (yield) and quality (diversity, micronutrient density) under current landholding sizes.

Presently, Andean farming systems are under great strains to provide sufficient output to meet the demands of the households that (partially) depend on agriculture for food security. Among other influences, land fragmentation [5254], interruption of communal rotation designs [55, 56], shortened fallow cycles [30, 55, 57, 58], soil degradation manifested as negative macronutrient and soil organic matter balances [43, 5963], pest and disease intensity [64, 65], expansion of agricultural activities into higher altitudes exposed to extreme weather [6668], and higher risk of harvest loss [69] may all compromise the basic capacity of agricultural systems and common development interventions to meet household-level nutrients demands. A deepened understanding of these intervening factors is beyond the scope of the current study. However, by taking the case of selected communities in central Peru, our research contributes with a much-needed estimate of current smallholder production system carrying capacity in terms of basic energy and micronutrient provision from agriculture. Through modeled scenarios we present a novel analysis of the capacity of specific agricultural interventions, or so-called food-based approaches, to increase the output of energy, iron and zinc from agriculture.

The purpose of our research is to examine whether agricultural output under current farming practice actually provides sufficient energy, iron and zinc output to meet household demand or input. We adopt the concept of carrying capacity to measure and compare the basic capacity of farming systems to cover household-level energy and micronutrient (iron and zinc) requirements through agriculture. Further, we explore variability among households and conditions associated with either positive or negative balances. To evaluate the contribution of food-based approaches to improve household-level balances, we analyze the effect of four agricultural intervention options. The outcome of these analyses are household-level balances: on-farm production output versus household-level demand for energy, iron and zinc.

The selected food-based interventions correspond to the strategies currently pursued by different development actors (NGOs and research for development centers): (i) enhancing potato yield, (ii) introducing biofortified potatoes, (iii) promoting guinea pigs and (iv) a combined approach. These interventions aim to eventually enhance smallholder household food and nutrition security with an emphasis on maternal child nutrition, support healthy livelihood outcomes and reduce vulnerability in the long-term [8, 70, 71]. The future implementation of such interventions would benefit from the present study. By providing an empirical assessment of the current carrying capacity of smallholder production systems and modeling specific interventions, our findings offer researchers and development practitioners information to feed into their nutrition and food security strategies.

Research methods

Research area

This study was conducted in four highland communities in the Huancavelica region, central Peru: Ccasapata, Ccollpaccasa, Sotopampa and Chopccapampa (Fig. 1). The communities were representative of the region with typical mixed production systems and high rates of child malnutrition and anemia. They were part of a McKnight Foundation-funded project researching the relationship between agrobiodiversity and nutrition and were selected on the basis of ethnicity, poverty and importance of agriculture. Geopolitically, the communities are part of Yauli district, Huancavelica province. Ethnically they belong to the so-called Chopcca nation, a Quechua speaking self-proclaimed indigenous group of 2000 households (9210 people), settled in an ex-hacienda adjudicated to them following a land reform in 1969 [72, 73]. Along with microfragmentation of landholdings, production conditions in Chopcca have been exacerbated through land degradation [74]. Within the study site, households manage mixed crop–livestock systems with fields located between 3600 and 4500 m of altitude. All agriculture is rain-fed. Important crops include potato (Solanum spp), oca (Oxalis tuberosa), olluco (Ullucus tuberosus), mashua (Tropaeolum tuberosum), barley (Hordeum vulgare), faba (Vicia faba), lupine (Lupinus mutabilis) and oats (Avena sativa). Livestock includes cattle, sheep, llama, pig, poultry and guinea pig [73, 74].
Fig. 1

Research site in Huancavelica region, central Peru

Huancavelica is one of the most food insecure regions in Peru [75, 76]. At the time of this study, stunting affected 19.6 % of children under five years old nationwide, yet in Huancavelica these figures reached 54.2 %, the highest in the country [77]. Stature as indicator of the overall nutritional condition among women in reproductive age was also lowest in Huancavelica at 3 cm under the national average [77]. Energy, vitamin A, iron and zinc coverage are reported to be lowest in rural areas including Huancavelica [78, 79]. In 2012, 49.4 % of children under five years of age in the district of Yauli were stunted [80]. According to the most recent study in the four Chopcca communities, 44.2 % of children under 3 years old who attended the local health facilities were stunted (Z score < −2 SD) and more than 75 % of children between 6 and 24 months old did not meet their daily recommended iron and zinc requirements [74, 81]. Such high incidence of malnutrition coexists with diverse crop–livestock portfolios and traditional farming practices [74, 82, 83].

Data collection

We conducted a detailed structured survey with 185 households with children between 6 and 36 months of age in close collaboration with local stakeholders (village authorities, public health posts). The study received ethics approval from the Research Ethics Committee of the Instituto de Investigación Nutricional (IIN). Informed consent was given verbally by each participant prior to the application of the survey. Trained teams implemented surveys shortly after the main harvests during the months of July and August (2010). All interviewers spoke Quechua. The survey was the main tool used to collect qualitative and quantitative information on household composition, crop–livestock portfolios, cropping areas (m2), production output (kg), among other parameters. Field sizes were checked through direct measurements. Potato yields were determined through direct measurement within 6 m2 unit areas at the field level. Measurements of 251 and 172 fields containing bred and landrace potato cultivars, respectively, were taken during the 2008, 2009 and 2012 growing seasons. These represented fields from 165 households. The number of animals for cattle, horse, llama, sheep, pig, poultry and guinea pig was recorded during the main survey in 2010.

Data calculations

On-farm production

For each household, the total annual on-farm production (=output) and household-level demand (=input) for energy in kilocalories (kcal), iron in milligrams (mg) and zinc in milligrams (mg) were calculated. Household-level output is a function of the number of fields/household, crop area/field (m2), crop yields/species (kg/m2) and the number of animals annually consumed. Crop and animal production totals (kg) were converted into energy (kcal), iron and zinc (mg) output based on content values provided by the Instituto de Investigacion Nutricional (IIN) and reported in the Peruvian Food Table [84]. Animal production output was based on household-level number of animals per species and conservative expert-validated calculations: (1) typical reproductive rates for each species; (2) offspring survival rates under the environmental conditions of the study site; (3) total number of animals (progenitors and offspring) potentially available for household consumption; (4) average meat and visceral mass in kg, including edible abdominal and thoracic organs, per animal for each species (Table 1). Total edible and available meat and viscera in kg were generated for each species reported per household. Cows and horses were not considered in calculations of meat and viscera output as these are generally not consumed but rather used for milk production and transport, respectively. Households’ number of cows therefore translated into total kg of cheese produced from average milk output. Chicken, apart from roosters, provide eggs (1 unit/day/chicken for 90 days of the year), meat and visceral mass.
Table 1

Livestock reproduction rates and weights in the Chopcca systems of Huancavelica region, central Peru

Species

Male-to-female ratio

Offspring per female per year

Meat weight (kg) per animal

Visceral weight (kg) per animal

Sheep

1:10

1

12.1

3.5

Cowa

0:1

0.5b

Llama

1:10

0.5b

28.9

15.5

Guinea Pig

1:6

5

0.75c

Chicken

1:3

0

2.6

0.3

Pig

0:1

5

52.5

5.3

aOnly dairy consumption; 94.5 kg of cheese/cow/year

b1 offspring every 2 years and not included in calculations

cIncludes visceral weight

Household demand

Annual household-level requirement for energy, iron and zinc is a function of household composition in terms of gender and age for each member multiplied by Daily Recommended Intakes (DRI) of energy (kcal), iron (mg) and zinc (mg) per person for the 365 days of the year [85, 86]. Daily energy requirements were calculated according to an activity level of 1.75 of the basal metabolic rate (BMR) for adult women assuming an average weight of 55 kg and an activity level of 1.90 of the BMR for adult men with an average weight of 60 kg, considering farm work in both cases. Thus, adult women were attributed a daily requirement of 2300 kcal for the 18–29 age range; 2250 kcal for the 30–59 age range; and 2050 kcal above 60 years of age. For women with infants under 12 months old, there was an additional daily requirement of 505 kcal for lactation. For adult men, the daily energy requirement was 3050 kcal for the 18–29 age range; 2950 kcal for the 30–59.9 age range; and 2450 kcal over 60 years of age. Energy requirements for male and female infants during the first year of life, and for boys and girls up to 18 years of age, were based on FAO/WHO/UNU standards [85]. Daily recommended requirements for iron and zinc were calculated assuming low (5 %) bioavailability for iron and low (15 %) bioavailability for zinc. These bioavailability levels were based on consumption data from the same households indicating a largely vegetable-sourced diet [74]. Minimal presence of iron-rich animal source foods does not support adopting a higher bioavailability [8688]. Recommended requirement levels of iron and zinc by gender and age group were based on FAO/WHO standards [86].

Nutrition balance

The difference between on-farm production (=output) and household demand (=input) for each individual household resulted in the nutrition balance for energy, iron and zinc. In turn, this balance is an indicator of the carrying capacity of the farming system in terms of its ability to provision sufficient energy, iron and zinc. The total output potentially available for each individual household follows a few assumptions. First, the output was considered to be readily available for consumption by households, without accounting for possible processing or post-harvest losses and produce sold. Second, production output as determined at the harvest and survey time was considered to be the only moment of food production for the household. This is generally true for rain-fed agriculture in Huancavelica, yet some households also obtain modest off-season harvests. Third, nutrition balances are solely based on total production output and household requirement without considering food handouts from aid programs or foodstuffs acquired through barter or monetary purchase. The assumptions are simplifications of the reality, but necessary and reasonable to answer the main research question: (i) whether (or not) household-level crop–livestock production output is able to cover the minimal household-level nutrition requirements and (ii) whether commonly promoted agricultural interventions potentially make a difference.

Statistical analyses

After screening for missing and incomplete data, 165 household surveys remained as final dataset. Crop and livestock production data, household composition, nutritional demand, energy and micronutrient balances were firstly analyzed through descriptive statistics and by correlation analysis of all the continuous independent variables measured (35 in total) in the study. R package FactomineR [89] was used to perform principal component analysis (PCA) in order to identify variables that contribute the most to the variation in the dataset and to detect relationships between them. Multiple and logistic regressions were performed in RStudio (version 0.99.902) to identify variables that significantly influence energy, iron and zinc balances. R Package LEAPS [90] was used to assist in the variable selection process for model building purposes for multiple regressions. Three different regression models (wherein energy, iron and zinc were treated as dependent variable individually) that consisted of variables selected by the “regsubsets” approach in the LEAPS package (variables with a high R2 and low Mallow’s Cp score) and from the PCA were developed and chosen after being statistically tested against the full model. Logistic regression was used to identify variables that significantly contribute to the odds of a household meeting coverage for all nutritional parameters simultaneously (energy, iron and zinc) as a binary dependent variable (1 = household passes three parameters; 0 = household fails three parameters). A total of 119 households that passed (n = 98) or failed (n = 21) this criterion were used for logistic regression. A balancing methodology employed by ROSE package [91] in R was adopted for this analysis, since the number of responses classified under each category (pass all parameters/fail all parameters) was skewed and imbalanced. Model building, selection and significance testing for logistic regression was performed on the dataset that was balanced using the ROSE algorithm. A model that was not statistically different from a full model, and which had a lower AIC score, in addition to obtaining a high area under ROC (receiver operating characteristic) curve of 0.96 (Additional file 1), with an accurate prediction rate of 93 % during validation, was chosen for further interpretation.

Agricultural intervention scenarios

Four agricultural (food-based) intervention scenarios were analyzed by following the same procedure to calculate the energy, iron and zinc output from household-level crop–livestock production, household-level requirements (=input) and consequent output/input balances. We assumed successful adoption of practices or technologies across all 165 households resulting in: (1) a 30 % yield increase of landrace and bred potatoes; (2) a 20 % areal adoption of biofortified potato varieties with iron and zinc fresh-weight concentrations1 twice as high as currently grown cultivars; (3) an addition of 1 male and 9 female guinea pigs in livestock production output; and (4) the combination of interventions 1, 2 and 3. For intervention 1, the 30 % yield increase was applied separately for household’s landrace and bred potato areas, as these cultivar groups have different yield levels. For intervention 2, 10 % of landrace and 10 % of bred potato household-level areas were assumed to be planted with biofortified varieties. For intervention 3, an ideal male-to-female ratio of 1:9 was chosen for guinea pigs to reproduce and generate offspring of which 50 % would be destined for household consumption.

Results

Household and crop–livestock system characteristics

Table 2 summarizes household characteristics, and Fig. 2 shows household member distribution. Standard deviation values in total average cropping area elude to ample differences among households (Fig. 3). One extreme concerned two households that did not cultivate any crops, yet raised livestock and reported commerce and transportation as off-farm activities. On the other extreme, two households by far exceeded the average total cropping areas (2.4 and 2.8 ha). Households from the community of Ccasapata had the lowest average total cropping area out of the four communities: 5901 (±3460) m2 compared to 7342 (±5662) m2 in Chopccapampa, 7718 (±3851) m2 in Sotopampa and 8399 (±4045) m2 in Ccollpaccasa.
Table 2

Main household and crop–livestock system characteristics

Sample characteristics

 

Number of households

165

Total number of household members

952

Demographic and socioeconomic characteristics

Household sizea

5.8 (±2.0)

Percentage of female household members (%)

51.2

Percentage of members 0–4 years old (%)

26.1

Percentage of members 5–10 years old (%)

20.4

Percentage of members 11–18 years old (%)

15.8

Percentage of members 19–35 years old (%)

27.4

Percentage of members > 35 years old (%)

10.3

Percentage of households with at least 1 migrant (%)

73.9

Number of months off-farm for migrating membersa

1.6 (±1.7)

Percentage of households with local commerce as main activity (%)

17.6

Percentage of households with handicrafts as main activity (%)

32.1

Percentage of households with livestock as main activity (%)

63.6

Agricultural characteristics a

Cultivated fields

9.1 (±3.3)

Crop species

6.0 (±1.5)

Animal species

3.9 (±1.5)

Tubers area (m2)

3605 (±2681)

Cereals area (m2)

2111 (±1496)

Legumes area (m2)

1523 (±1172)

Total cropped area (m2)

7239 (±4253)

aMean (±SD)

Fig. 2

Distribution of the total number of household members per household (N = 165)

Fig. 3

Distribution of the total cropping area per household for the predominant agricultural season (N = 165)

Production features by crop species are summarized in Table 3. The landrace potatoes were the most diverse crop in terms of the number of cultivars being grown, with one household growing as many as 160 different cultivars. Indeed high infraspecific diversity of the potato crop is a distinctive feature of Huancavelica region [75, 83, 92]. Olluco, mashua and oca cultivars—up to a maximum of 12, 14 and 10 per household, respectively—are frequently grown in close proximity to potato. Most households only plant a single variety of barley and oats. Among the legumes, one variety of lupine is commonly cultivated. Faba, with up to a maximum of 17 distinct cultivars grown at the household level, is common.
Table 3

Crop species production indicators at the household level (N = 165)

Crops

Households planting (%)

No. of fields

Field size (m2)

Yielda (ton/ha)

No. of cultivars

Ave.

SD ±

Ave.

SD ±

Ave.

SD ±

Ave.

SD ±

Landrace potato

96.4

1.8

1.0

1323.1

983.8

14.7

5.3

5.7

4.0

Bred potato

85.5

1.2

0.4

885.6

614.4

23.1

11.3

1.1

0.8

Olluco

79.4

1.0

0.2

260.6

229.8

6.3

5.7

1.6

0.9

Mashua

71.5

1.0

0.1

241.5

240.7

9.1

8.5

1.2

0.6

Oca

23.6

1.0

0.2

220.1

197.7

7.6

7.0

1.8

1.2

Faba

90.3

1.4

1.1

571.1

469.2

1.7

1.3

2.5

1.5

Lupine

73.9

1.3

0.6

918.8

641.7

0.8

1.9

1.0

0.2

Barley

95.2

1.6

0.7

1066.7

667.3

2.3

1.9

1.0

0.2

Oats

68.5

1.2

1.1

724.7

579.7

1.7

1.9

1.0

0.0

aYields for faba, lupine, barley and oats were calculated in their dry state

Households in the study site rise up to seven livestock species that serve multiple purposes (Table 4). Livestock species, except horses, are a source of food but are also used as mode of transport and for fertilizer, fuel, fiber, milk and cheese production. Yet households typically only slaughter animals on special occasions. Livestock is generally kept free-range, and herding is a common task of women and children. Use for home consumption in Huancavelica is occasional. Bigger livestock such as cattle and sheep represent an important asset that can be turned into cash.
Table 4

Species-based characteristics of livestock production (N = 165)

Livestock

Households raising (%)

Total no. of animals

Ave.

SD±

Cattle

78.2

2.6

1.6

Horse

49.7

1.4

0.6

Llama

12.7

5.8

4.3

Sheep

67.3

9.3

8.1

Pig

37.6

2.0

1.9

Poultry

80.6

4.3

3.5

Guinea Pig

63.0

7.0

5.5

Energy, iron and zinc balances under status quo management

Figure 4 shows household-level energy, iron and zinc balances. We excluded one outlier for compact visualization (164 total bars). The average energy, iron and zinc balances across all households were +10,826 kcal, +41 mg and +37 mg per day over the average basal household-level requirements. While the majority of households obtained sufficient energy from their crop–livestock systems to meet their requirements, the gap was wider for iron and zinc. In 13.9, 36.6 and 23.6 % of households, production output did not meet the household-level energy, iron and zinc requirements, respectively, under current crop–livestock management. We found household-level energy balances were highly correlated with iron (r = 0.79) and zinc (r = 0.92) balances (p < 0.001). Ninety-eight (out of 99) households who met both their iron and zinc requirements also met their energy requirements. Conversely, out of 142 households who met their energy requirements 98 (69 %) also met their iron and zinc requirements. Clearly, meeting energy demands is more attainable than meeting essential micronutrients under current crop–livestock production systems.
Fig. 4

Household-level energy, iron and zinc balances in ascending order and as proportions (+) or (−) of actual household requirements (n = 164). The x-axis (y = 0) represents basal household-level demand for full coverage

Crop and livestock contribution to energy, iron and zinc requirements is presented as a proportion of household-level requirements (Fig. 5) for those households whose production systems did not meet minimum requirements (so-called negative coverage) and households whose production systems met or surpassed minimum requirements (so-called positive coverage). Livestock production contributed modestly to household energy, iron and zinc coverage. Overall, crops as compared to livestock accounted for 179, 122 and 144 % versus 13, 4 and 12 % of the energy, iron and zinc production output from agriculture, respectively, based on the average household-level balances. The PCA (Fig. 6) further indicated that individual crop outputs and per capita cropping areas for main staples (tubers, legumes, cereals) were strongly and positively correlated with the investigated parameter balances and together accounted for most of the variation observed in the dataset, in comparison with other variables such as those related to livestock and household that were also measured in this study.
Fig. 5

Contribution of crop and livestock production to the actual energy, iron and zinc balance for households with a positive and negative coverage for each nutritional parameter. Dotted line (y = 100 %) represents basal household-level demand for full coverage

Fig. 6

PCA plot consisting of all the variables (35) measured in this study, showing their relative influence on the variation observed in the dataset, which is indicated as  % on the first dimension (Dim 1) and the second dimension (Dim 2). This PCA also gives an indication of the grouping between all the variables measured. Variable labels: “Kcal_balance” = energy balance; “Fe_balance” = iron balance; “Zn_balance” = zinc balance; “Total_crops_m2” = total cropping area; “m2_person” = total cropping area per capita; “Tubers_m2_pcapita” = per capita area cultivated with tubers; “Cereals_m2_pcapita” = per capita area cultivated with cereals; “Legumes_m2_pcapita” = per capita area cultivated with legumes; “No_migrant” = number of household members migrating; “Mig_months” = number of months off-farm for migrating members. Livestock labels refer to total number of animals. Crop labels refer to total surface area in square meters (m2) and total outputs in kilograms (kg). Arrow length is proportionate to the contribution made by an individual variable to the variation in the dataset. Directionality relative to other variables illustrates the nature of correlations (positive or negative) among variables. Crop outputs and per capita cropping areas (tubers, cereals, legumes) are shown to not only be largely driving variation in the data space (long arrows) but to also be positively correlated to energy, iron and zinc balances (same direction). The fact that livestock and household (size, migration) parameters are placed relatively far from the remaining variables further suggests that crop parameters have the strongest and most influential relations to household balance outcomes

Multiple regression analysis revealed that household size, landrace potato area (m2), bred potato area (m2), barley, faba and oats outputs (kg) significantly influence each of the balances (Table 5; Added-variable plots provided as Additional file 2). As expected, household size was a negatively correlated predictor across all models. This anti-correlation was also evident in the PCA, where the variable household size is placed in opposition to (all the) balances in the data space (Fig. 6). Household-level landrace and bred potato areas (m2) and outputs (kg) were significantly correlated (p < 0.001) with respective household energy (r = 0.78/0.65) and zinc balances (r = 0.58/0.55), and less so with iron balances (r = 0.38/r = 0.50). Household-level barley output (kg) was significantly correlated (p < 0.001) with the iron (r = 0.75) and zinc balance (r = 0.62) of the households growing these crops, and less so with the household-level energy balance (r = 0.48). Faba was modestly correlated (p < 0.001) with iron (r = 0.51) and zinc balance (r = 0.41). Oats and lupine, although still important in terms of proportion of households who grow them and area coverage per household (Table 3), were not significantly correlated with energy, iron and zinc balance (r < 0.35). Mashua was moderately correlated (p < 0.001) with iron balance (r = 0.59). Our regression models showed that cattle and sheep made a positive and significant contribution to energy and zinc balances, respectively. In terms of iron, livestock sources were not detected as significant.
Table 5

Regression coefficients, standard errors (SE), p values and significance under three models for energy, iron and zinc balance (N = 165)

Predictor

Coefficient

SE

p value

Sig.

Energy balance

    

Intercept

519,107.16

105,978.64

0.000

***

Household size

−809,849.58

18,288.05

<2e−16

***

Landrace potato (m2)

1720.79

18.92

<2e−16

***

Bred potato (m2)

2311.17

38.56

<2e−16

***

Barley (kg)

3957.88

168.97

<2e−16

***

Faba (kg)

2708.56

357.95

0.000

***

Lupine (kg)

2430.56

311.17

0.000

***

Oats (kg)

2992.72

413.65

0.000

***

Cattlea

173,366.37

20,712.54

0.000

***

Adjusted R 2

0.992

   

Iron balance

    

Intercept

6100.00

2600.00

0.018

*

Household size

−11,000.00

440.00

<2e−16

***

Landrace potato (m2)

5.80

0.48

<2e−16

***

Bred potato (m2)

13.00

0.93

<2e−16

***

Barley (m2)

3.10

1.10

0.004

**

Mashua (kg)

19.00

7.70

0.017

*

Barley (kg)

98.00

5.10

<2e−16

***

Faba (kg)

66.00

8.70

0.000

***

Oats (kg)

31.00

10.00

0.003

**

Adjusted R 2

0.943

   

Zinc balance

    

Intercept

4677.76

604.52

0.000

***

Household size

−5028.19

104.29

<2e−16

***

Landrace potato (m2)

5.45

0.11

<2e−16

***

Bred potato (m2)

7.07

0.22

<2e−16

***

Barley (kg)

31.85

0.96

<2e−16

***

Faba (kg)

27.37

2.03

<2e−16

***

Lupine (kg)

45.66

1.76

<2e−16

***

Oats (kg)

39.62

2.35

<2e−16

***

Sheepa

255.16

25.70

<2e−16

***

Adjusted R 2

0.987

   

aLivestock output is in number of animals

p < 0.05; ** p < 0.01; *** p < 0.001

In Table 6, we show results for the logistic regression model where the binary dependent variable was the odds of a household meeting energy, iron and zinc requirements given a set of predictor variables. Surprisingly, household size was not a significant predictor of the odds ratio of a household meeting its energy, iron and zinc requirements through family farming. The odds of a household meeting its energy, iron and zinc basal requirements decreases by a factor of 0.945 (not significant) for every additional family member, whereas it significantly increases by a factor of 1.014 (p < 0.001) for every square meter of tubers grown, a factor of 1.010 (p < 0.01) for every square meter of legumes and a factor of 1.004 (p < 0.05) for every square meter of cereals. The highest contribution to tubers area comes from landrace potato, bred potato and mashua. Legumes area is mostly represented by lupine and faba, while cereals area is mainly attributed to barley. This analysis suggests that the likelihood of a household achieving all its basal balance requirements is positively influenced by increasing its per capita cultivated areas for tubers, legumes and cereals. Furthermore, these results are in agreement with the PCA, which indicates that per capita cropping areas (“Tubers_m2_pcapita,” “Cereals_m2_pcapita,” “Legumes_m2_pcapita” in Fig. 6) make a larger relative contribution to variation and are positively correlated with energy, iron and zinc balances (“Kcal_balance,” “Fe_balance,” “Zn_balance” in Fig. 6).
Table 6

Balanced (optimal) model of logistic regression with coefficients (odds ratio), standard errors (SE), p values and significance levels (N = 119)

Predictor

Coefficienta

SE

p value

Sig.

 

Intercept

0.001

2.398

0.005

**

 

Household size

0.945

0.164

0.732

  

Tubers area per capita (m2)

 1.014

0.004

0.001

***

 

Cereals area per capita (m2)

1.004

0.002

0.036

*

 

Legumes area per capita (m2)

1.010

0.003

0.001

**

 

aOdds ratio of household meeting all nutritional parameters (energy, iron, zinc)

p < 0.05; ** p < 0.01; *** p < 0.001

Household differences behind the nutrition balances

In the previous sections, we presented the factors that were most strongly associated with balances in terms of a household´s crop–livestock production system meeting its energy, iron and zinc requirements. In light of those findings, we summarize the main differences between households with a negative coverage whose production system did not provide sufficient output (n = 21), and households with a positive coverage whose production system met demand or provided a surplus (n = 98) for energy, iron and zinc (Table 7). Why did certain households cover their needs, while others didn’t? Landrace and bred potato outputs, followed by cereals like barley and legumes like faba, represent the bulk of households’ production and source of energy, iron and zinc. This is a consequence of cropping areas and yield levels. Households with a positive coverage, or those whose production systems met minimum parameter requirements, had significantly higher cropping areas by individual species and as grand total. Household size was not significantly different between households with positive and negative coverage (p = 0.175). The opposite was true for cultivated surface area per household member (area per capita) which was significantly different (p < 0.001). Household size was generally large across both groups, but households with a negative coverage for energy, iron and zinc from family farming were constrained by small cropping areas and consequent limited production outputs per household member. In that context—unavailable land—adding one more mouth to feed to household-level crop–livestock production will result in a negative nutrition balance, as should be expected and was previously made evident through the relations of variables in the PCA and regressions.
Table 7

Summary of differences for households with a positive and negative coverage from family farming for all three parameters (energy, iron and zinc)

Variables

Households with positive coverage for energy, iron and zinc (n = 98)

Households with negative coverage for energy, iron and zinc (n = 21)

Mean diff.

Ave.

SD±

Min.

Max.

Ave.

SD±

Min.

Max.

p value

Household size

5

2

3

10

6

3

2

11

0.175

Landrace potato (kg)

3968

3157

0

20,213

1370

1353

0

3675

0.000

Bred potato (kg)

2519

2557

0

20,213

750

997

0

3609

0.000

Mashua (kg)

133

151

0

900

47

60

0

180

0.000

Olluco (kg)

127

148

0

900

32

26

0

60

0.000

Oca (kg)

32

67

0

360

5

22

0

100

0.002

Barley (kg)

394

249

0

1320

103

118

0

360

0.000

Oats (kg)

80

97

0

480

33

53

0

210

0.003

Faba (kg)

132

133

0

630

37

50

0

160

0.000

Lupine (kg)

64

136

0

1200

20

25

0

84

0.003

Tubers area per capita (ha)

0.08

0.06

0.02

0.38

0.02

0.01

0

0.04

0.000

Cereals area per capita (ha)

0.05

0.03

0.004

0.16

0.01

0.01

0

0.05

0.000

Legumes area per capita (ha)

0.04

0.03

0

0.17

0.01

0.01

0

0.05

0.000

Total crop area (ha)

0.88

0.44

0.24

2.78

0.31

0.21

0

0.78

0.000

Total area per capita (ha)

0.17

0.08

0.05

0.56

0.05

0.02

0.01a

0.10

0.000

People per ha

7

3

2

20

29

37

10a

182

0.000

Cattle no.

2

2

0

9

1

1

0

5

0.021

Sheep no.

7

8

0

41

2

3

0

12

0.000

Llama no.

1

3

0

18

0

1

0

5

0.050

Pig no.

1

2

0

13

0

1

0

2

0.001

Poultry no.

4

4

0

30

2

2

0

5

0.000

Guinea pig no.

5

5

0

29

3

3

0

10

0.035

Migrants per household

1

1

0

3

1

1

0

3

0.602

Months migrating

1.23

1.56

0

12

0.81

0.93

0

3

0.110

Local commerce main actb

(%)

15.3

  

(%)

23.8

   

Handicrafts main actb

(%)

34.7

  

(%)

19.0

   

Livestock main actb

(%)

63.3

  

(%)

42.9

   

aDoes not include 2 households without cropping areas

bCategorical variables calculated as percentage of households in each group (positive/negative coverage)

There is some overlap in the range of total cropping areas of households with a positive and negative coverage of energy, iron and zinc demands from family farming. However, their total areas and per capita areas by staple group (tubers, cereals, legumes) point to significantly different means. The variable “people per hectare” also reveals a striking difference for households with a positive and negative coverage. Factors such as differences in yield and available household labor can affect the household-level production output per area unit to sustain more people.

Although livestock sources of energy, iron and zinc contributed modestly to households’ overall balance, differences between households with positive and negative coverage were nonetheless significant for stocks of sheep, poultry and pig. This suggests that in addition to land as a household asset, households with a positive coverage had access to other forms of assets allowing them to sustain more livestock, such as time, labor, grazing area, feed or monetary resources. Compared to households with negative coverage, a greater proportion of households with positive coverage reported livestock raising as a main activity. Some households with a positive coverage compensated the absence of one crop (min. values of 0 in Table 7) with another, thus maintaining an overall positive balance. For instance, one family did not cultivate landrace potatoes but allocated large areas to bred potato (3750 m2) and barley (3125 m2). On the other hand, two households with a negative coverage did not cultivate any crops but reported employment in commerce (local store) and transportation (inter-provincial driver). Thirty-five percent (35 %) of households with a positive coverage were involved in artisanal (woven) crafts as source of income, compared to 19 % of households with negative coverage. When non-agricultural activities are able to complement households’ maintenance of their crop–livestock portfolios, nutrition balances can shift positively through food access. Number of household migrants and months off-farm were not significantly different between households with a positive or negative coverage (p values of 0.602 and 0.110 in Table 7), nor did they correlate significantly to households’ balance outcomes (r < 0.11 and p > 0.15 across all balances). This may suggest a lack of reinvestment of migration-based income in production and on-farm nutrient provision. However, our limited sample, lack of precise income data and of dietary measurements of energy, iron and zinc coverage via other, off-farm routes of food access do not allow us to further pursue such potential associations.

Nutrition balances under intervention scenarios

Current practices (=status quo) and development intervention scenario outcomes on household-level energy, iron and zinc balanced are compared in Table 8. A 30 % yield increase of potato reduced the total proportion of households with a negative coverage for energy, iron and zinc from family farming by 4.8, 7.9 and 7.2 %, respectively. While the balance deficit decreased across the three nutrition parameters, they remained negative, being insufficient to overcome the overall nutrition gap. A 20 % areal adoption of biofortified potatoes did not modify average energy balance, as it solely involved iron and zinc. It resulted, however, in a 3.7 and 3.6 % reduction of the total proportion of households with a negative coverage for iron and zinc, while their balances only modestly increased. The introduction of 10 additional guinea pigs (and offspring during one season) for each household has minimal effect on nutrition outcomes. Only an additional 1.8 and 1.2 % of households will shift to basal iron and zinc coverage, while average balances remain negative at nearly the same level as status quo.
Table 8

Energy, iron and zinc balance outcomes in status quo and intervention scenarios

Scenario

% Households with negative coverage (n = 165)

Average household balance per daya

(n = 21)

Energy

Iron

Zinc

Energy (kcal)

Iron (mg)

Zinc (mg)

Status quo

13.9

37.6

23.6

−3903

−90

−34

1. Potato yield increaseb

9.1

29.7

16.4

−1999

−77

−27

2. Biofortified potatoesc

13.9

33.9

20.0

−3903

−87

−32

3. Microlivestockd

13.9

35.8

22.4

−3858

−89

−33

4. Combined (1 + 2 + 3)

9.1

26.1

15.2

−1954

−77

−25

aCalculations based on households with negative coverage targeted by interventions. Minus 484 (−) sign indicates deficit

b30 % yield increase in bred and landrace potatoes

CBiofortified potatoes adopted on 20 % of potato cropping area

dIntroduction and adoption of guinea pigs: 1 male and 9 females

Considering energy, iron and zinc (all at once), the combined strategy is the most effective. However, negative household balances are still not overcome. Of the first three scenarios seen in isolation, a 30 % yield increase of potato was most impactful in terms of reducing the energy, iron and zinc deficit. Nevertheless, the combined scenario is probably a best case scenario of what development agencies can achieve even though it is still far from eradicating deficits.

Discussion

Nutrient balances under current crop–livestock systems

Food security encompasses four essential dimensions: availability, access, utilization and stability [93, 94]. Fundamental in the availability dimension is not only the quantity but also the nutritional quality of foods [95]. We have conducted an in-depth analysis of the availability pillar of food security rather than the access, utilization and stability dimensions. Particularly, we have focused on the capacity of contemporary high-altitude smallholder production systems to cover the energy, iron and zinc requirements at the household level. Our results show that 86 % of households were able to cover basal energy requirements through on-farm production. Yet, the farming system’s iron and zinc output would not meet basal household-level demand for 37 and 24 % of households. Considering energy, iron and zinc simultaneously, only 59 % of households in the study site would be able to cover their requirements based on agricultural output.

The capacity of family farming to provide households with their basic nutritional needs varies widely among households. Contrasting positive and negative nutrition balances at the household-level offer valuable lessons. Firstly, differences in household-level cropping area ultimately determined production outputs. Households with fewer fields and smaller size fields are less likely to meet basal energy, iron and zinc requirements. This clearly indicates the limits to the carrying capacity of farming systems to potentially cover the nutrient demands of the households who depend on family farming as a means of (partial) self-sufficiency. Also, the cultivated area available to households and production outputs are a consequence of land access and the intensive on-farm labor implicit and fundamental in these systems, which is not equally available to households [96, 97]. Secondly, household size, although not directly correlated to energy, iron and zinc balances, influences the capacity of crop–livestock systems to cover requirements among households already strained by limited land availability. We demonstrated this association in our multiple regression models where household size was a significant predictor of the household-level energy, iron and zinc balance. In the logistic regression model, although not significant, the odds of a household meeting its energy, iron and zinc requirements also decreased with increasing household size.

Diversity in agricultural production has been associated with nutritional diversity and improved dietary quality, especially in the context of smallholder farming systems [98102]. While overall crop and livestock production diversity is high in the study area, many households would not be able to cover the basal micronutrient (iron and zinc) demand through self-production. On-farm species richness does not, according to several studies, linearly translate into food security, dietary diversity and improved nutrition [103105]. In addition to the effects of landholding and household size, the actual energy and micronutrient composition of foods determined household balance outcomes. Importantly, landrace and bred potatoes, faba and barley were highly influential in the context of the cropping portfolios we have examined, in terms of their energy, iron and zinc provisioning capabilities across all households. The opposite is true of animal-based foods, which are encouraged as part of a diverse and nutrient-dense diet that makes essential micronutrients like iron and zinc readily available to people [106109]. In this study, we have shown that, even for households with positive coverage, the contribution of livestock to energy, iron and zinc demands is modest compared to crop-based energy, iron and zinc. Nevertheless, the significant differences between households with positive and negative coverage in terms of animal stocks underscore assets other than food that are likely available to households with a positive coverage, such as labor, grazing areas, feed or cash once they are sold (i.e., cattle, sheep).

Nutrient balances under intervention scenarios

Keeping cropping areas and household sizes the same, even the most commonly promoted intervention scenarios were not sufficient to close the gap in energy, iron and zinc provision from family farming. The mixed intervention strategy that combined yield increase and biofortification of potatoes with the introduction of microlivestock (i.e., guinea pig) reduced the percentage of households with a negative coverage from family farming to a minimum of 9.1 % for energy, 26.1 % for iron and 15.2 % for zinc. On its own, a bred and landrace potato yield increase of 30 % led to results that were comparatively low (9.1, 29.7, 16.4 %, respectively). Promoting management options for a higher production output per area unit for the main staple crops has a higher probable impact on raising the energy, iron and zinc output from the system compared to biofortification or promotion of microlivestock given the intervention options we have examined. Nonetheless, nutrient provision from agricultural interventions underlying food-based approaches to nutrition will ultimately depend on actual land availability, which is already strained in the region of Huancavelica where the research was conducted. It is precisely due to limited production capacity, particularly small and fragmented land holdings, that common interventions aimed at yield increases, biofortification and/or microlivestock promotion do not significantly shift realities. Households with a negative coverage of energy, iron and zinc from their farm have fewer production assets (land, livestock). Proportionally, the limited scale of new technologies or interventions they adopt as compared to those households with more sizeable assets and surplus production will only contribute marginally toward improving their nutrition situation. In addition to the assumption that smallholder households targeted by agricultural interventions toward food-based approaches to nutrition have the land capacity to attain a positive coverage from family farming, another factor that may be overlooked is labor scarcity and the increasing shift of labor responsibilities on women, who are also primary caregivers for infants [20, 26, 100]. In the context of migratory patterns and intensifying agricultural workloads for women, interventions may actually undermine household nutrition. From the standpoint of land and on-farm labor availability and capacity, the gap in energy, iron and zinc provision from agriculture among the most resource-poor households is likely to persist, unless development organizations seek complementary ways of improving food security and nutritional outcomes, for example, through combining agricultural interventions with off-farm employment opportunities.

Study limitations

In order to pursue the analysis that we have presented in this study, assumptions were necessary. Crop production outputs were calculated without considering potential post-harvest losses, sales or additional, off-season production. Livestock were deemed available for consumption assuming that households would not sell or save their animal stocks as reserves. In the context of Chopcca crop–livestock systems, households generally raise their animals for uses other than consumption or prefer to sell them for cash thereby increasing their purchasing power to access other goods. Thus, even under the conservative reproductive rates that we derived, our approximations of livestock production to nutrient output reflect an ideal consumption scenario. The energy, iron and zinc content of crop and livestock output was assumed to remain stable and available to households without taking into account preparation, cooking or processing losses that could affect the actual micronutrients available for consumption.

Our study limited itself to the detailed analysis of energy, iron and zinc output from family farming and its contribution to meeting household-level demands. It thus does not deal with actual food intake, which is beyond the scope of the research here reported.2 We used DRI assuming low bioavailability for iron and zinc, considering the local dietary patterns. This may have been an underestimation in some cases, although the presence of animal source foods (medium and high iron and zinc bioavailability) in the diet was infrequent and minimal. How the production of energy and micronutrients is actually allocated at the intra-household level, and whether or not they reach the most vulnerable household members (i.e., infants) is beyond the scope of this study.

Other factors that we have not explored, such as off-farm foods, accessible markets, labor migration and increasingly non-agricultural and diversified incomes, are important drivers of changing food systems and smallholder diets [23, 103, 105]. In particular, household-level information about off-farm food purchases and on-farm produce sold would have allowed us to quantify households’ energy and micronutrient availability from on- and off-farm sources more accurately. Considering that the primary research objective was to determine crop–livestock systems’ energy and micronutrient coverage solely based on household-level production, our study has been able to make a significant advance to inform agricultural strategies underlying food-based approaches to nutrition. As an important next step, we recommend investigating an additional model to characterize the nutrition inputs available to smallholder households as off-farm contributions via agricultural income generation and off-farm employment.

Conclusion

Agricultural interventions supporting food-based approaches to nutrition are deployed in regions most affected by high rates of undernutrition, such as Huancavelica in the central Andes of Peru, under two underlying assumptions: (1) Smallholder agriculture has the capacity to provision household-level energy and micronutrient needs; (2) innovations can make a substantial difference among the most vulnerable households. In this study, we have demonstrated that both assumptions can either be positively reaffirmed or rejected, depending to a large extent on the size of land holdings and family size. While all households studied managed typical smallholder farming systems, not all family farms had the carrying capacity to supply sufficient energy, iron and/or zinc for the households that depend on them. We demonstrate that for poorer households, the current practices and even improvement in productive capacity do not satisfy household-level demand for energy, iron and zinc. Yet, the research also validates the sufficiency of the nutrient and energy output conferred by agriculture and the strategy of enhancing local production for resource-sufficient households.

A negative coverage of iron and zinc from family farming is a reality for 21 % of the households studied. This level of undercoverage from farming is not significantly reduced by the most commonly promoted interventions, precisely because households with negative coverage do not have the assets (land, labor) to generate a big enough effect from the key innovations. Balances for production output versus household demand for energy, iron and zinc remain negative among the most resource-strained households across all intervention options. As has been assessed in previous studies, well-intentioned interventions aimed at enhancing agricultural production toward better food-based nutrition do not necessarily translate into positive outcomes [110112].

For the most resource-poor households, current practices and an enhancement in productive capacity do not satisfy energy and essential micronutrient coverage from the family farm. Conversely, more resourceful households possess sufficient land to meet, if not surpass, their energy, iron and zinc requirements via self-production. Importantly, these households manage cropping areas that are on average 2.8-fold larger compared to households with negative coverage. Further, these households own more livestock which, if not directly consumed, they can maintain as capital and future liquid assets. Such sources of modest income, in addition to that provided through off-farm employment, could in turn be enhancing the crop productivity of the households with a positive coverage for energy, iron and zinc from family farming through purchases of agricultural inputs (i.e., fertilizer, seeds). Access to agricultural and mixed livelihood strategies that include the cultivation of key staples, modest livestock reserves and non-agricultural sources of household income are important conditions for smallholder farmers to meet household-level energy and micronutrient requirements.

Crop and livestock production systems are still an essential part of a dynamic and evolving Andean food system. As consumption patterns, sources of food, off-farm income strategies and markets are changing, the on-farm provisioning of energy and micronutrients from agriculture continue to be important [105, 113, 114]. Yet, non-traditional livelihood activities in the rural Andes, part-time farming and feminization of agriculture are also challenging perceptions of subsistence agriculture and smallholder reliance on agriculture-centered options alone [22, 23, 25, 26, 115, 116]. In order for future research and interventions to effectively target nutrition outcomes, we recommend a mixed approach and attention to support smallholders’ food and nutrition security via the opportunities offered by other routes of income diversification and food acquisition.

Footnotes
1

Fresh-weight, as opposed to dry-weight, iron and zinc concentrations were used because potato outputs were weighed in their raw, post-harvest state. No significant differences have been found between the iron and zinc concentrations of raw versus cooked potatoes [117, 118].

 
2

Assessment of daily food and nutrient intakes in the children of these families, using the 24-hour recall methodology, is addressed in a different study as part of the same McKnight Foundation project [74, 81].

 

Abbreviations

AIC: 

Akaike information criterion

BMR: 

basal metabolic rate

DRI: 

daily recommended intake

FAO: 

Food and Agriculture Organization

IIN: 

Instituto de Investigacion Nutricional

KCAL: 

kilocalories

MG: 

milligrams

NGO: 

non-governmental organization

PCA: 

principal component analysis

ROC: 

receiver operating characteristic

SD: 

standard deviation

UNU: 

United Nations University

WHO: 

World Health Organization

Declarations

Authors’ contributions

SDH conceived the study. AA led its development, performing the data analysis, interpretation and preparation of the manuscript. HCK provided guidelines on foods’ energy and micronutrient contents and human requirements. MS, RC and EO designed the household surveys, coordinated implementation and collected field data. RC and EO supported with crop and livestock data interpretation. DB performed statistical analyses and revised results interpretation. SDH contributed to writing the manuscript. HCK and MS reviewed and made editorial comments of the manuscript draft. All authors read and approved the final manuscript.

Acknowledgements

We would like to acknowledge the collaboration of field-survey teams, community authorities, local health posts and smallholder farmers across the four communities that were part of this study. The research was supported by Grupo Yanapai professionals who trained survey teams and oversaw the implementation of household-level surveys. The Instituto de Investigacion Nutricional (IIN) provided nutritional requirement standards and guidelines. In particular, we are indebted to Lizette Ganoza from IIN for providing energy, iron and zinc contents for all crops and livestock analyzed in this study. We would also like to thank Gabriela Burgos from the International Potato Center’s Genetic Crop Improvement Program for answering our questions related to potato biofortification. We are grateful to Jens Oldeland from the University of Hamburg for his statistical advice and to Toss Gascoigne from Econnect Communication for his helpful review of the manuscript draft.

Competing interests

The authors declare that they have no competing interests.

Availability of data and material

The data that support the findings of this study are available in the Open Science Framework repository “Chopcca agri-nutrition”: https://osf.io/wgtqr/.

Ethics approval and consent to participate

The study received ethics approval from the Research Ethics Committee of the Instituto de Investigación Nutricional (IIN). Informed consent was given verbally by each research participant prior to the application of any research procedures (survey).

Funding

The McKnight Foundation’s Collaborative Crop Research Program financially supported this research under the project “Agrobiodiversity and nutrition for the food security of the Chopcca Communities of Huancavelica.” The International Potato Center (CIP) provided a grant for the main author to conduct her research in Peru.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Agricultural Sciences, Universidad de Antioquia
(2)
Instituto de Investigación Nutricional
(3)
Grupo Yanapai
(4)
International Center for Tropical Agriculture, Agricultural Genetics Institute

References

  1. Food and Agriculture Organization of the United Nations. Combating micronutrient deficiencies: food-based approaches. 1st ed. Rome: CAB International and Food and Agriculture Organization of the United Nations; 2011.Google Scholar
  2. Bioversity International. Diversifying food and diets: using agricultural biodiversity to improve nutrition and health. 1st ed. Oxon: Routledge; 2013.Google Scholar
  3. LoretDeMola C, Quispe R, Valle GA, Poterico JA. Nutritional transition in children under five years and women of reproductive age: a 15-years trend analysis in Peru. PLoS ONE. 2014;9:e92550.View ArticleGoogle Scholar
  4. World Health Organization. Nutrition landscape information system (NLIS) country profile indicators: interpretation guide. Geneva: World Health Organization; 2010.Google Scholar
  5. Sobrino M, Gutiérrez C, Cunha AJ, Dávila M, Alarcón J. Desnutrición infantil en menores de cinco años en Perú: tendencias y factores determinantes. Rev Panam Salud Publica. 2014;35:104–12.PubMedGoogle Scholar
  6. Iniciativa contra la Desnutrición Infantil (IDI). Balance y Desafíos Sobre las Acciones del Gobierno para Reducir la Desnutrición Infantil. Perú–2014. Lima; 2014.Google Scholar
  7. Mejía Acosta A, Haddad L. The politics of success in the fight against malnutrition in Peru. Food Policy. 2014;44:26–35.View ArticleGoogle Scholar
  8. Ministerio de Desarrollo e Inclusión Social. Estrategia Nacional de Desarrollo e Inclusión Social “Incluir Para Crecer.” Lima: Ministerio de Desarrollo e Inclusión Social; 2014.Google Scholar
  9. Stifel D, Alderman H. The “glass of milk” subsidy program and malnutrition in Peru. Policy Research Working Paper 3089. Washington DC: World Bank; 2003.Google Scholar
  10. Stifel D, Alderman H. Targeting at the margin: the “glass of milk” subsidy programme in Peru. J Dev Stud. 2005;41:839–64.View ArticleGoogle Scholar
  11. Davidsson L, Walczyk T, Zavaleta N, Hurrell RF. Improving iron absorption from a Peruvian school breakfast meal by adding ascorbic acid or Na2EDTA. Am J Clin Nutr. 2001;73:283–7.PubMedGoogle Scholar
  12. López De Romaña D, Salazar M, Hambidge M, Penny ME, Peerson JM, Krebs NF, Brown KH. Longitudinal measurements of zinc absorption in Peruvian children consuming wheat products fortified with iron only or iron and 1 of 2 amounts of zinc. Am J Clin Nutr. 2005;81:637–47.PubMedGoogle Scholar
  13. Waters HR, Penny ME, Creed-Kanashiro HM, Robert RC, Narro RO, Willis J, Caulfield LE, Black RE. The cost-effectiveness of a child nutrition education programme in Peru. Heal Policy Plan. 2006;21:257–64.View ArticleGoogle Scholar
  14. Castro J, Chirinos D. Impact of a comprehensive intervention on food security in poor families of central highlands of Peru. Food Public Health. 2015;5:213–9.Google Scholar
  15. Hawkes C, Ruel MT. Value Chains for Nutrition. 2020 Conference Paper. Washington, DC: International Food Policy Research Institute (IFPRI); 2011.Google Scholar
  16. Ordinola M. Innovaciones y desarrollo: El caso de la cadena de la papa en el Perú. Rev Latinoam la Papa. 2011;16:39–57.Google Scholar
  17. Crespeigne E, Olivera E, Ccanto R, Scurrah M. Exploración de las estrategias y practicas de una comunidad campesina de los Andes centrales frente a los riesgos extremos asociados al cambio climático. In: Ames P, Caballero V, editors. Perú: El Problema Agrario en Debate. SEPIA XIII. Lima: Seminario Permanente de Investigación Agraria (SEPIA); 2010. p. 260–90.Google Scholar
  18. Young KR. Andean land use and biodiversity: humanized landscapes in a time of change. Ann Missouri Bot Gard. 2009;96:492–507.View ArticleGoogle Scholar
  19. Condori B, Hijmans RJ, Ledent JF, Quiroz R, Liu JH. Managing potato biodiversity to cope with frost risk in the high andes: a modeling perspective. PLoS ONE. 2014;9:e81510.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Zimmerer KS, Carney JA, Vanek SJ. Sustainable smallholder intensification in global change? Pivotal spatial interactions, gendered livelihoods, and agrobiodiversity. Curr Opin Environ Sustain. 2015;14:49–60.View ArticleGoogle Scholar
  21. Gilles JL, Thomas JL, Valdivia C, Yucra ES. Laggards or leaders: conservers of traditional agricultural knowledge in Bolivia. Rural Sociol. 2013;78:51–74.View ArticleGoogle Scholar
  22. Bebbington AJ. Landscapes of possibility? Livelihood and intervention in the production of Andean Landscapes. In: Wescoat JL, Johnston DM, editors. Political economies of landscape change: places of integrative power. Dordrecht: Springer; 2008. p. 51–76.View ArticleGoogle Scholar
  23. Zimmerer KS. Conserving agrobiodiversity amid global change, migration, and nontraditional livelihood networks: the dynamic uses of cultural landscape knowledge. Ecol Soc. 2014;19(2):1.View ArticleGoogle Scholar
  24. Deere CD. The feminization of agriculture? Economic restructuring in rural Latin America. United Nations Research Institute for Social Development (UNRISD) Occasional Paper. Geneva: UNRISD; 2005.Google Scholar
  25. The World Bank Group. Women in agriculture. The impact of male out-migration on women’s agency, household welfare, and agricultural productivity. Washington DC; 2016.Google Scholar
  26. Valdivia C, Gilles JL, Turin C. Andean pastoral women in a changing world: opportunities and challenges. Rangelands. 2013;35:75–81.View ArticleGoogle Scholar
  27. Katz E. The changing role of women in the rural economies of Latin America. In: Davis B, editor. Current and emerging issues for economic analysis and policy research. Volume I: Latin America and the Caribbean. Rome: Food and Agriculture Organization of the United Nations; 2003. p. 31–66.Google Scholar
  28. Wiegers ES, Hijmans RJ, Herve D, Fresco LO. Land use intensification and disintensification in the Upper Cañete Valley, Peru. Hum Ecol. 1999;27:319–39.View ArticleGoogle Scholar
  29. Bussink CB, Hijmans RJ. Land-Use Change in the Cajamarca Catchment, Peru, 1975–1996. In: Scientist and farmer: partners in research for the 21st century. Program Report, 1999–2000. Lima: International Potato Center;2001. p. 421–8.Google Scholar
  30. De Haan S, Juárez H. Land use and potato genetic resources in Huancavelica, central Peru. J Land Use Sci. 2010;5:179–95.View ArticleGoogle Scholar
  31. Rocha JM. Agricultural intensification, market participation, and household demography in the Peruvian Andes. Hum Ecol. 2011;39:555–68.View ArticleGoogle Scholar
  32. Brush SB. The concept of carrying capacity for systems of shifting cultivation. Am Anthropol. 1975;77:799–811.View ArticleGoogle Scholar
  33. Buringh P. Availability of agricultural land for crop and livestock production. In: Pimentel D, Hall CW, editors. Food and natural resources. San Diego: Academic Press; 1989. p. 69–83.View ArticleGoogle Scholar
  34. Pimentel D, Armstrong LE, Flass CA, Hopf FW, Landy RB, Pimentel MH. Interdependence of food and natural resources. In: Pimentel D, Hall CW, editors. Food and natural resources. San Diego: Academic Press; 1989. p. 31–48.View ArticleGoogle Scholar
  35. Sayre NF. The genesis, history, and limits of carrying capacity. Ann Assoc Am Geogr. 2008;98:120–34.View ArticleGoogle Scholar
  36. Pulliom HR, Haddad NM. Address of the past president. human population growth and the carrying capacity concept. Bull Ecol Soc Am. 1994;75:141–57.Google Scholar
  37. Harris JM, Kennedy S. Carrying capacity in agriculture: global and regional issues. Ecol Econ. 1999;29:443–61.View ArticleGoogle Scholar
  38. Pimentel D. Energy inputs in food crop production in developing and developed nations. Energies. 2009;2:1–24.View ArticleGoogle Scholar
  39. Cassidy ES, West PC, Gerber JS, Foley JA. Redefining agricultural yields: from tonnes to people nourished per hectare. Environ Res Lett. 2013. doi:https://doi.org/10.1088/1748-9326/8/3/034015.Google Scholar
  40. Fedoroff NV. Food in a future of 10 billion. Agric Food Secur. 2015;4:11.View ArticleGoogle Scholar
  41. Garnett T, Godfray J. Sustainable intensification in agriculture. Navigating a course through competing food system priorities. Food Climate Research Network and the Oxford Martin Programme on the Future of Food. Oxford: University of Oxford; 2012.Google Scholar
  42. Tittonell PA. Farming systems ecology. Towards ecological intensification of world agriculture. Wageningen: Wageningen University; 2013.Google Scholar
  43. Fonte SJ, Vanek SJ, Oyarzun P, Parsa S, Quintero DC, Rao IM, Lavelle P. Pathways to agroecological intensification of soil fertility management by smallholder farmers in the Andean Highlands. Adv Agron. 2012;116:125–84.View ArticleGoogle Scholar
  44. Sands DC, Morris CE, Dratz EA, Pilgeram AL. Elevating optimal human nutrition to a central goal of plant breeding and production of plant-based foods. Plant Sci. 2009;177:377–89.View ArticlePubMedGoogle Scholar
  45. Ordinola M. Agricultura, nutricion y seguridad alimentaria: Un enfoque diferente. Agroenfoque. 2015;29:65–7.Google Scholar
  46. Bellon MR, Gotor E, Caracciolo F. Assessing the effectiveness of projects supporting on-farm conservation of native crops: evidence from the High Andes of South America. World Dev. 2015;70:162–76.View ArticleGoogle Scholar
  47. Ordinola M, Fonseca C, Vela AM, Devaux A. Desarrollando Innovaciones para la Seguridad Alimentaria y Nutricional con Base en la Biodiversidad. Lima: Centro Internacional de la Papa; 2014.View ArticleGoogle Scholar
  48. Kroschel J, Mujica N, Alcazar J, Cañedo V, Zegarra O. developing integrated pest management for potato: experiences and lessons from two distinct potato production systems of Peru. In: He Z, Larkin R, Honeycutt W, editors. Sustainable potato production: global case studies. 1st ed. Dordrecht: Springer; 2012. p. 419–50.View ArticleGoogle Scholar
  49. Kroschel J, Alcazar J, Cañedo V, Miethbauer T, Zegarra O, Cordoba L, Gamarra C. Producción de papa orgánica en la región andina del Perú: el manejo integrado de plagas lo hace posible. In: Henriquez P, Li Pun H, editors. Innovaciones de impacto: Lecciones de la agricultura familiar en America Latina y el Caribe. 1st ed. San Jose: IICA, BID; 2013. p. 165–81.Google Scholar
  50. Ortuño N, Navia O, Claros M, Gutierrez C, Barja D, Arandia W, Crespo F. Aporte a la soberanía alimentaria en los Andes bolivianos: exploración microbiana y desarrollo de bioinsumos en comunidades campesinas. In: Henriquez P, Li Pun H, editors. Innovaciones de impacto: Lecciones de la agricultura familiar en América Latina y el Caribe. 1st ed. San Jose: IICA, BID; 2013. p. 17–26.Google Scholar
  51. Peru REDESA-CARE. La Familia Saludable En La Chacra Integral. 1st ed. Lima: USAID-CARE Peru; 2006.Google Scholar
  52. Velazco J, Pinilla V. Peasants households´ access to land and income diversification, the Peruvian Andean case, 1998-2000. In: Hillbom E, Svensson P, editors. Agricultural transformation in a global history perspective. 1st ed. Oxon: Routledge; 2013. p. 253–80.Google Scholar
  53. Garcia M, Gilles J, Yucra E, Rojas K. Changing production systems in three Andean ecosystems in the face of environmental change. La Paz; 2015.Google Scholar
  54. McDowell JZ, Hess JJ. Accessing adaptation: multiple stressors on livelihoods in the Bolivian highlands under a changing climate. Glob Environ Change. 2012;22:342–52.View ArticleGoogle Scholar
  55. Parsa S. Explaining the dismantlement of indigenous pest management in the Andes. PhD thesis. Davis: University of California Davis; 2009.Google Scholar
  56. Parsa S. Native herbivore becomes key pest after dismantlement of a traditional farming system. Am Entomol. 2010;56:242–51.View ArticleGoogle Scholar
  57. Orlove BS, Godoy R. Sectoral fallowing systems in the Central Andes. J Ethnobiol. 1986;6:169–204.Google Scholar
  58. Pestalozzi H. Sectoral fallow systems and the management of soil fertility: the rationality of indigenous knowledge in the High Andes of Bolivia. Mt Res Dev. 2000;20:64–71.View ArticleGoogle Scholar
  59. Swinton SM, Quiroz R. Poverty and the deterioration of natural soil capital in the Peruvian Altiplano. Environ Dev Sustain. 2003;5:477–90.View ArticleGoogle Scholar
  60. Dercon G, Deckers J, Govers G, Poesen J, Sanchez H, Vanegas R, Ramirez M, Loaiza G. Spatial variability in soil properties on slow-forming terraces in the Andes region of Ecuador. Soil Tillage Res. 2003;72:31–41.View ArticleGoogle Scholar
  61. Zimmerer KS. Soil erosion and labor shortages in the Andes with special reference to Bolivia, 1953–91: implications for “conservation-with-development”. World Dev. 1993;21:1659–75.View ArticleGoogle Scholar
  62. Barrowclough M, Stehouwer R, Alwang J, Gallagher R, Mosquera VHB, Dominguez JM. Conservation agriculture on steep slopes in the Andes: promise and obstacles. J Soil Water Conserv. 2016;71:91–102.View ArticleGoogle Scholar
  63. Goodman-Elgar M. Evaluating soil resilience in long-term cultivation: a study of pre-Columbian terraces from the Paca Valley, Peru. J Archaeol Sci. 2008;35:3072–86.View ArticleGoogle Scholar
  64. Giraldo D, Juarez H, Perez W, Trebejo I, Yzarra W, Forbes G. Severity of the potato late blight (Phytophthora infestans) in agricultural areas of Peru associated with climate change. Rev Peru Geo-Atmosf. 2010;2:56–67.Google Scholar
  65. Kroschel J, Sporleder M, Tonnang HEZ, Juarez H, Carhuapoma P, Gonzales JC, Simon R. Predicting climate-change-caused changes in global temperature on potato tuber moth Phthorimaea operculella (Zeller) distribution and abundance using phenology modeling and GIS mapping. Agric For Meteorol. 2013;170:228–41.View ArticleGoogle Scholar
  66. Skarbø K, Vandermolen K. Maize migration: key crop expands to higher altitudes under climate change in the Andes. Clim Dev. 2015. doi:https://doi.org/10.1080/17565529.2015.1034234.Google Scholar
  67. Tovar C, Seijmonsbergen AC, Duivenvoorden JF. Monitoring land use and land cover change in mountain regions: an example in the Jalca grasslands of the Peruvian Andes. Landsc Urban Plan. 2013;112:40–9.View ArticleGoogle Scholar
  68. Haller A. The, “sowing of concrete”: Peri-urban smallholder perceptions of rural–urban land change in the Central Peruvian Andes. Land use policy. 2014;38:239–47.View ArticlePubMedPubMed CentralGoogle Scholar
  69. Sietz D, Mamani Choque SE, Lüdeke MKB. Typical patterns of smallholder vulnerability to weather extremes with regard to food security in the Peruvian Altiplano. Reg Environ Chang. 2012;12:489–505.View ArticleGoogle Scholar
  70. Comisión Multisectorial de Seguridad Alimentaria y Nutricional-Ministerio de Agricultura y Riego. Estrategia Nacional de Seguridad Alimentaria y Nutricional 2013–2021. Lima: Ministerio de Agricultura y Riego; 2013.Google Scholar
  71. Creed-Kanashiro H, Penny ME, Carrasco M, Marin M. Baseline survey project window of opportunity in Peru. Lima: CARE-Peru; 2011.Google Scholar
  72. Instituto Nacional de Estadística e Informática. Sistema de Consulta de Datos de los Censos Nacionales 2007: XI de Población y VI de Vivienda. Lima: Instituto Nacional de Estadística e Informática; 2007. p. 2007.Google Scholar
  73. Roel Mendizabal P, Martinez Vivanco M. Los Chopcca de Huancavelica. Etnicidad y Cultura en el Perú Contemporaneo. 1st edition. Lima: Ministerio de Cultura; 2013.Google Scholar
  74. Scurrah M, De Haan S, Olivera E, Ccanto R, Creed H, Carrasco M, Veres E, Barahona C. Ricos en agrobiodiversidad pero pobres en nutrición: Desafíos de la mejora de la seguridad alimentaria en comunidades de Chopcca, Huancavelica. In: Asensio RH, Eguren F, Ruiz M, editors. Perú: El Problema Agrario en Debate. SEPIA XIV. Lima: Seminario Permanente de Investigación Agraria (SEPIA); 2012. p. 362–407.Google Scholar
  75. De Haan S, Núñez J, Bonierbale M, Ghislain M. Multilevel agrobiodiversity and conservation of Andean potatoes in Central Peru: species, morphological, genetic, and spatial diversity. Mt Res Dev. 2010;30:222–31.View ArticleGoogle Scholar
  76. Creed-Kanashiro H, Astete LR, Abad MA, Marin M, Bartolini R. Linea de Base Nutricional Peru. Lima: Centro Internacional de la Papa; 2014.View ArticleGoogle Scholar
  77. Instituto Nacional de Estadística e Informática. Perú: Encuesta Demográfica y de Salud Familiar-ENDES 2011. Lima: Instituto Nacional de Estadística e Informática; 2012.Google Scholar
  78. Instituto Nacional de Salud-Centro Nacional de Alimentación y Nutricion. Informe de Resultados sobre Consumo de Alimentos en Niños de 6 a 35 Meses según MONIN 2008-2010. Lima: Ministerio de Salud; 2012.Google Scholar
  79. Instituto Nacional de Estadística e Informática. Lactancia y Nutrición de Niñas, Niños y Madres. In: Encuesta Demográfica y de Salud Familiar 2013. Lima: Instituto Nacional de Estadística e Informática; 2014. p. 275–313.Google Scholar
  80. Instituto Nacional de Salud-Centro Nacional de Alimentación y Nutricion. Sistema de Información del Estado Nutricional: Indicadores Nutricionales Nivel Distrital en Niños Menores de 59/35 Meses Perù 2012, OMS. Lima: Ministerio de Salud; 2012.Google Scholar
  81. Veres Jorda E. La Agrobiodiversidad como Estrategia para el Fortalecimiento de la Seguridad Alimentaria: Estudio de Caso en las Comunidades Chopccas (Huancavelica, Perú). MSc thesis. Valencia: Universidad Politecnica de Valencia; 2011.Google Scholar
  82. Graham RD, Welch RM, Saunders DA, Monasterio IO, Bouis HE, Bonierbale M, De Haan S, Burgos G, Thiele G, Liria R, Meisner CA, Beebe SE, Potts MJ, Kadian M, Hobbs PR, Gupta RK, Twomlow S. Nutritious subsistence food systems. Adv Agron. 2007;92:1–74.View ArticleGoogle Scholar
  83. De Haan S. Potato diversity at height: multiple dimensions of farmer-driven in situ conservation in the Andes. PhD thesis. Wageningen: Wageningen University; 2009.Google Scholar
  84. Instituto Nacional de Salud. Tablas Peruanas de Composición de Alimentos. 8th ed. Lima: Ministerio de Salud; 2009.Google Scholar
  85. Food and Agriculture Organization of the United Nations. Human energy requirements. Report of a Joint FAO/WHO/UNU Expert Consultation. Rome: Food and Agriculture Organization of the United Nations; 2004.Google Scholar
  86. Food and Agriculture Organization of the United Nations. Human vitamin and mineral requirements. Report of a Joint FAO/WHO Expert Consultation. Bangkok, Thailand. Rome: Food and Agriculture Organization of the United Nations; 2001.Google Scholar
  87. Hunt JR. Bioavailability of iron, zinc, and other trace minerals from vegetarian diets. Am J Clin Nutr. 2003;78:633S–9S.PubMedGoogle Scholar
  88. Bach Kristensen M, Hels O, Morberg CM, Marving J, Bügel S, Tetens I. Total zinc absorption in young women, but not fractional zinc absorption, differs between vegetarian and meat-based diets with equal phytic acid content. Br J Nutr. 2006;95:963–7.View ArticleGoogle Scholar
  89. Lê S, Josse J, Husson F. FactoMineR: an R package for multivariate analysis. J Stat Softw. 2008;25:1–18.View ArticleGoogle Scholar
  90. Thomas L, Miller A. Package “leaps”. Regression subset selection. Version 2.9. CRAN R Project. 2009. p. 1–8.Google Scholar
  91. Lunardon N, Menardi G, Torelli N. ROSE: a package for binary imbalanced learning. R Journal. 2014;6(1):79–89.Google Scholar
  92. Centro Internacional de la Papa. Catalogo de Variedades de Papa Nativa de Huancavelica-Peru. 1st ed. Lima: Centro Internacional de la Papa; 2006.Google Scholar
  93. Food and Agriculture Organization of the United Nations. An introduction to the basic concepts of food security. In: Food Security information for action. Practical Guides. 2008. http://www.fao.org/docrep/013/al936e/al936e00.pdf. Accessed 22 Dec 2015.
  94. Fanzo J. Strengthening the engagement of food and health systems to improve nutrition security: synthesis and overview of approaches to address malnutrition. Glob Food Sec. 2014;3:183–92.View ArticleGoogle Scholar
  95. Harris J, Meerman J, Aberman NL. Introduction to conceptual issues related to agriculture, food security, and nutrition. In: Aberman NL, Meerman J, Benson T, editors. Mapping the linkages between agriculture, food security and nutrition in Malawi. Washington: International Food Policy Research Institute (IFPRI); 2015. p. 1–7.Google Scholar
  96. Zimmerer KS. Labor shortages and crop diversity in the Southern Peruvian Sierra. Geogr Rev. 1991;81:414–32.View ArticleGoogle Scholar
  97. Mayer E, Glave M. “Alguito para ganar” (a little something to earn): profits and losses in peasant economies. Am Ethnol. 1999;26:344–69.View ArticleGoogle Scholar
  98. DeClerck FAJ, Fanzo J, Palm C, Remans R. Ecological approaches to human nutrition. Food Nutr Bull. 2011;32:S41–50.View ArticlePubMedGoogle Scholar
  99. Remans R, Flynn DFB, Declerck F, Diru W, Fanzo J, Gaynor K, Lambrecht I, Mudiope J, Mutuo PK, Nkhoma P, Siriri D, Sullivan C, Palm CA. Assessing nutritional diversity of cropping systems in African villages. PLoS ONE. 2011;6:e21235.View ArticlePubMedPubMed CentralGoogle Scholar
  100. Jones AD, Cruz Agudo Y, Galway L, Bentley J, Pinstrup-Andersen P. Heavy agricultural workloads and low crop diversity are strong barriers to improving child feeding practices in the Bolivian Andes. Soc Sci Med. 2012;75:1673–84.View ArticlePubMedPubMed CentralGoogle Scholar
  101. Jones AD. The production diversity of subsistence farms in the Bolivian Andes is associated with the quality of child feeding practices as measured by a validated summary feeding index. Public Health Nutr. 2014;18:329–42.View ArticlePubMedGoogle Scholar
  102. Silvia S, Sabine D, Patti K, Wiebke F, Maren R, Ianetta M, Carlos QF, Mario H, Anthony N, Nicolas N, Joash M, Lieven C, Cristina RM. Households and food security: lessons from food secure households in East Africa. Agric Food Secur. 2015;4:23.View ArticleGoogle Scholar
  103. Oyarzun PJ, Borja RM, Sherwood S, Parra V. Making sense of agrobiodiversity, diet, and intensification of smallholder family farming in the Highland Andes of Ecuador. Ecol Food Nutr. 2013;52:515–41.View ArticlePubMedGoogle Scholar
  104. Patel K, Gartaula H, Johnson D, Karthikeyan M. The interplay between household food security and wellbeing among small-scale farmers in the context of rapid agrarian change in India. Agric Food Secur. 2015;4:16.View ArticleGoogle Scholar
  105. Sibhatu KT, Krishna VV, Qaim M. Production diversity and dietary diversity in smallholder farm households. PNAS. 2015;112:10657–62.View ArticlePubMedPubMed CentralGoogle Scholar
  106. Gibson RS, Hotz C. Dietary diversification/modification strategies to enhance micronutrient content and bioavailability of diets in developing countries. Br J Nutr. 2001;85:S159–66.View ArticlePubMedGoogle Scholar
  107. Berti PR, Fallu C, Cruz Agudo Y. A systematic review of the nutritional adequacy of the diet in the Central Andes. Rev Panam Salud Publica. 2014;34:314–23.Google Scholar
  108. Humphries DL, Behrman JR, Crookston BT, Dearden K, Schott W, Penny ME. Households across all income quintiles, especially the poorest, increased animal source food expenditures substantially during recent Peruvian economic growth. PLoS ONE. 2014;9(11):e110961.View ArticlePubMedPubMed CentralGoogle Scholar
  109. Neumann C, Harris DM, Rogers LM. Contribution of animal source foods in improving diet quality and function in children in the developing world. Nutr Res. 2002;22:193–220.View ArticleGoogle Scholar
  110. Berti PR, Krasevec J, Fitzgerald S. A review of the effectiveness of agriculture interventions in improving nutrition outcomes. Public Health Nutr. 2004;7:599–609.View ArticlePubMedGoogle Scholar
  111. Ramakrishnan U, Nguyen P, Martorell R. Effects of micronutrients on growth of children under 5 y of age: meta-analyses of single and multiple nutrient interventions. Am J Clin Nutr. 2009;89:191–203.View ArticlePubMedGoogle Scholar
  112. Masset E, Haddad L, Cornelius A, Isaza-Castro J. Effectiveness of agricultural interventions that aim to improve nutritional status of children: systematic review. BMJ. 2012;344:d8222.View ArticlePubMedPubMed CentralGoogle Scholar
  113. Guyomard H, Darcy-Vrillon B, Esnouf C, Marin M, Russel M, Guillou M. Eating patterns and food systems: critical knowledge requirements for policy design and implementation. Agric Food Secur. 2012;1:13.View ArticleGoogle Scholar
  114. Tilman D, Clark M. Global diets link environmental sustainability and human health. Nature. 2014;515:518–22.View ArticlePubMedGoogle Scholar
  115. Gray CL. Rural out-migration and smallholder agriculture in the southern Ecuadorian Andes. Popul Environ. 2009;30:193–217.View ArticleGoogle Scholar
  116. Lennox E, Gowdy J. Ecosystem governance in a highland village in Peru: facing the challenges of globalization and climate change. Ecosyst Serv. 2014;10:155–63.View ArticleGoogle Scholar
  117. Bonierbale M, Burgos G, Amoros W, Salas E, Scurrah M, de Haan S, Zum Felde T. Progress and prospects for potato biofortification: diversity, retention, breeding, and delivery. In: IV Reuniao de biofortificacao no Brasil. Conference proceedings. Teresina: Embrapa Meio-Norte; 2011. p. 1–5.Google Scholar
  118. Burgos G, Amoros W, Morote M, Stangoulis J, Bonierbale M. Iron and zinc concentration of native Andean potato cultivars from a human nutrition perspective. J Sci Food Agric. 2007;87:668–75.View ArticleGoogle Scholar

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