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. Author manuscript; available in PMC: 2026 May 14.
Published in final edited form as: AIDS. 2025 May 14;39(11):1650–1655. doi: 10.1097/QAD.0000000000004234

Effect of a climate-smart intervention on agriculture and nutrition of people with HIV: a cluster-randomized trial

Tammy M NICASTRO a,b, A Rain MOCELLO a, Elly WEKE c, Elizabeth A BUKUSI c, Edward A FRONGILLO d, Craig R COHEN a, Sheri D WEISER a, Suneetha KADIYALA b, Helen A HARRIS-FRY b
PMCID: PMC12324949  NIHMSID: NIHMS2079439  PMID: 40372058

Abstract

Objective:

To evaluate impacts of Shamba Maisha, a climate-smart agriculture intervention, on agricultural and nutrition outcomes of people with HIV.

Design:

Secondary analysis of a parallel, non-blinded cluster-randomized controlled trial.

Setting:

Western Kenya.

Participants:

Clusters were clinics providing HIV services to >350 patients. Participants were adults aged 18–60 years in a patient support group, receiving antiretroviral therapy for ≥6 months, with access to land and water source, and moderate to severe food insecurity and/or BMI <18.5 kg/m2.

Intervention:

A market-rate loan (175 USD) for irrigation pump and agricultural inputs, and a series of training sessions on climate-smart agriculture and financial management. Trial registration: NCT02815579.

Outcomes:

Primary outcomes: dietary diversity (count of food groups/day) and crop diversity (count of crops/year). Secondary outcomes: low mid-upper arm circumference (MUAC <24.5cm), value of agricultural production (1000 KSH/year), farmland use (hectares). Outcomes were assessed at 0, 6, 12, 18, and 24 months. Analyses were by intention-to-treat.

Results:

Between June 2016 and December 2017, 16 clinics were randomly allocated to intervention or control (1:1 ratio). 720 participants were enrolled (366 intervention; 354 control), and 677 participants evaluated (344 intervention; 333 control). Relative to control, the intervention increased crop diversity (mean difference-in-difference: 1.9 [95% CI 1.52, 2.32]) and value of agriculture production (3.9 [0.05, 7.73]), and reduced farmland use (−0.9 [−1.34, −0.42]). There was no effect on diet diversity (−0.2 [−0.37, 0.03]) or MUAC (OR 0.76 [0.39, 1.45]).

Conclusions:

Climate-smart agriculture interventions can improve agricultural outcomes but may benefit from additional nutrition-sensitivity to improve nutrition.

Keywords: Agriculture, HIV, Kenya, livelihoods, diet

INTRODUCTION

It is estimated that 9.2 million people with HIV did not access antiretroviral therapy (ART) in 2023 [1]. Food insecurity poses a critical barrier to ART access and adherence [2], as taking ART on an empty stomach can cause adverse side effects [3–5] and costs of accessing ARTs can be prohibitive [6]. Recognizing this, UN agencies recommend integrating food and nutrition support into HIV treatment [7,8]. Traditionally, short-term food supplementation (commonly 18–24 months) has been used [7,9]. Livelihoods-based solutions are also recommended for long-term sustainability [10].

In sub-Saharan Africa, climate-smart agricultural interventions offer a promising solution [11,12]. Three agricultural livelihoods interventions have been evaluated among people with HIV: two in Kenya [13–15] and one in Malawi [16]. These studies showed improvements in food security [13,17] and nutrition [16], but there is limited evidence on agricultural impacts. As climate change poses challenges for farmers [18–21], climate-smart agriculture interventions are critical.

This study presents a secondary analysis of Shamba Maisha, a climate-smart agricultural intervention targeted to smallholder farmers with HIV in Western Kenya. We previously reported results for HIV health [15] and other outcomes [22][23]. Here we estimate effects on agricultural and nutritional outcomes by season and gender.

Methods

Setting

The study was conducted in Kisumu, Homa Bay, and Migori Counties of Western Kenya. Subsistence farming is the predominant source of income and nutrition [24], and this region experiences frequent flooding, unpredictable rain, severe droughts, and rising temperatures [25,26].

Study design

This is a parallel pair-matched cluster randomized controlled trial (RCT registration: NCT02815579) [15].

Participant enrollment

Health facilities were eligible for inclusion if they provided HIV services to >350 active patients. Between June 2016 and December 2017, participants were enrolled through health facilities. See Supplemental Figure 1 for the study timeline.

Trial participants in control and intervention arms were screened according to the same eligibility criteria: 1) aged 18–60 years, 2) received ART for ≥ 6 months, 3) had access to farmland and a permanent water source for irrigation, 4) were moderately to severely food insecure and/or a body-mass index <18.5 kg/m2, 5) expressed willingness to save the down payment (2000 KSH, ~20 USD) for the agricultural loan, 6) spoke Kiswahili, Dhuluo, or English, and 7) belonged to or willing to join a patient support group.

Consent and ethics

Written informed consent was obtained. Ethics approval was obtained from Kenya Medical Research Institute (KEMR1/SERU/CMR/3027) and University of California San Francisco (IRB 14–15592).

Randomization and blinding

Health facilities were matched into pairs by facility type, sub-county, main irrigation source, soil composition, and market accessibility. The statistician randomized facilities within pairs to the control or intervention arm using a computer program that randomly assigned numbers. Participants, enumerators and – for this paper – analysts were not blinded to allocation.

Intervention

Key components of the Shamba Maisha intervention were: placement into farmer groups of 8–15 people, training on climate-smart agricultural methods, financial training, and a loan to purchase a manual irrigation pump and other farming inputs such as seeds and fertilizers.

Farmer groups were facilitated by local agricultural trainers with master’s degrees. Farmer groups received eight three-hour training modules, plus a final review session. Climate-smart topics included: use of organic fertilizer, intercropping, crop rotation, water management, shade structures to protect seedlings, and raised rows and contour cropping to prevent soil erosion. Financial training covered benefits of savings accounts to cope with financial or weather shocks, and making agricultural investments. Farmer groups also met twice per month without the agricultural trainer to support each other and discuss financial goals. The market-rate loan (175 USD; 4% interest) was used to purchase the Super MoneyMaker irrigation pump by Kick Start International and to purchase seeds, fertilizer, and pesticides.

Control

Control arm did not receive the intervention but could receive a modified version of the intervention after study completion.

Data collection

A team of interviewers collected data at 0, 6, 12, 18, and 24 months after enrolment. Each intervention cluster was assessed at the same time as its paired control. Data quality was assured with training, and data checks on range, logic, and missing values.

Outcomes

Primary outcomes were: crop diversity (count of unique crops grown the previous year), and dietary diversity (daily count of 10 food groups [27], averaged over a 3-month recall). Secondary outcomes were: crop value in 1000 KSH per year, where the value of each crop is unit price multiplied by units produced; total hectares farmed the previous year; and prevalence of low mid-upper arm circumference (MUAC ≤24.5 cm) [28].

Sample Size

The target sample size was determined for the outcomes reported in the main paper, giving a target of eight clusters and 352 participants per arm.

Statistical Analysis

Our main analyses followed a pre-specified analysis plan and were by intention-to-treat (ITT). We used difference-in-difference (DiD) linear and logistic regression models [15] using maximum likelihood estimations. Participants and facilities were treated as random effects; trial arm, time point, and the interaction between arm and time (for the DiD estimate) were fixed effects. Given high follow-up rates, we did not impute missing data.

Additionally, we conducted per-protocol analyses on primary outcomes, comparing outcomes of those who received the intervention (attended ≥6 farmer trainings and ≥1 financial trainings) against the control. We performed two sub-analyses on primary outcomes. The first assessed seasonal effects. To avoid losses to sample size, we constructed baseline and endline scores for both rainy and dry season by matching baseline data (month 0, and month 6 for diet diversity) to seasonally matched 18- or 24-month endline data. Given the time lag between enrolment and the loan administration, we considered diet data from month 6 to be a reasonable baseline proxy. The second sub-analysis was by gender.

Results

The trial enrolled 16 clinics and 720 participants, with 366 participants in intervention clinics (344 completed study participants) and 354 control participants (333 completed study participants) (Supplemental Figure 2).

Sociodemographic characteristics at baseline were well balanced across arms (Supplemental Table 1). We enrolled slightly more women (55%; mean age 38 (SD 9.4) years) than men (45%; mean age 43 (SD 8.2) years).

Main results

Relative to control, the intervention increased diversity and value of crop production, while reducing the use of land to achieve these improvements (Table 1). Crop diversity increased by 1.9 unique crops [95% CI 1.52, 2.32], crop production value increased by 3,891 KSH per year [49, 7734], and land use declined by 0.9 hectares [−1.34,−0.42]. Frequency of crops produced at endline are reported in Supplemental Table 2, showing a shift towards production of nutrient-dense foods such as kale, cowpea, spinach, and sweet potato among intervention participants versus control.

Table 1.

Effect of Shamba Maisha on agriculture and nutrition outcomes

Control Intervention Intervention vs control
Baseline Endline Baseline Endline
N Mean (SD) N Mean (SD) N Mean (SD) N Mean (SD) DiD 95% CI P value
Primary Outcomes
Crop diversity score 347 3.6 (1.8) 325 3.0 (1.7) 358 3.7 (1.9) 332 5.1 (2.6) 1.9 1.52, 2.32 <0.001
Dietary diversity score 353 5.2 (1.0) 332 5.4 (0.9) 366 5.4 (1.0) 342 5.4 (1.0) −0.2 −0.37, 0.03 0.11
Secondary outcomes
Crop value, 1000 KSH/year * 328 21.7 (17.6) 313 21.4 (19. 3) 337 18.6 (18.0) 323 22.2 (18.5) 3.9 0.05, 7.73 0.047
Land farmed (hectares) 354 2.2 (1.5) 334 2.6 (3.5) 366 2.2 (1.6) 344 1.7 (2.0) −0.9 −1.34,−0.42 <0.001
N n (%) N n (%) N n (%) N n (%) OR 95% CI P value
Low MUAC (<24.5 cm) 354 50 (14%) 333 48 (14%) 364 44 (12%) 339 33 (9.7%) 0.76 0.39, 1.45 0.40
Open in a new tab

DiD = Difference-in-difference; OR = Odds ratio.

*

Results are similar using a log-transformed outcome (DiD 0.49; 95% CI 0.07, 0.91; p=0.022).

There was no evidence of intervention effects on dietary diversity (DiD −0.2 [−0.37, 0.03]), or low MUAC (odds ratio 0.76 [0.39, 1.45]).

Additional analyses

Per-protocol analyses show similar results for crop diversity (DiD 2.3 [1.90, 2.79]) and dietary diversity (−0.04 [−0.26, 0.19]) compared with the intent-to-treat estimates (Table 2). The intervention showed a similarly positive effect on crop diversity in the rainy (DiD 1.8 [95% CI 1.36, 2.18]) and dry seasons (1.9 [1.61, 2.28]). The intervention had no effect on diet diversity during the rainy (0.04 [−0.22, 0.31]) or dry season (−0.05 [−0.23, 0.13]).

Table 2.

Effect of Shamba Maisha on crop and diet diversity by level of exposure, season, and gender of participant

Outcome Subgroup Control Intervention Intervention vs control
Baseline Endline Baseline Endline
N Mean (SD) N Mean (SD) N Mean (SD) N Mean (SD) DiD 95% CI P value
Per protocol
Crop diversity High attendance 347 3.6 (1.8) 325 3.0 (1.7) 212 3.8 (1.9) 208 5.6 (2.5) 2.3 1.90, 2.79 <0.001
Diet diversity High attendance 353 5.2 (1.0) 332 5.4 (0.9) 217 5.4 (1.0) 211 5.5 (1.0) −0.04 −0.26, 0.19 0.75
By season
Crop diversity Rainy season 354 3.5 (1.9) 354 2.9 (1.8) 366 3.6 (1.9) 366 4.7 (2.8) 1.8 1.36, 2.18 <0.001
Dry season 354 0.8 (1.4) 354 0.8 (1.3) 366 1 (1.6) 366 3.0 (2.3) 1.9 1.61, 2.28 <0.001
Diet diversity Rainy season 72 5.2 (0.9) 72 5.5 (0.9) 115 5.3 (1.1) 95 5.3 (0.9) 0.04 −0.22, 0.31 0.74
Dry season 240 5.1 (1.1) 157 5.4 (0.9) 202 5.4 (1.0) 156 5.5 (1.0) −0.05 −0.23, 0.13 0.61
By gender
Crop diversity Women 186 3.7 (1.9) 178 3.1 (1.8) 197 3.8 (1.8) 188 4.9 (2.4) 1.7 1.19, 2.26 <0.001
Men 157 3.5 (1.7) 143 2.9 (1.6) 160 3.6 (2.0) 143 5.3 (2.7) 2.2 1.57, 2.77 <0.001
Diet diversity Women 190 5.1 (1.0) 180 5.3 (1.0) 201 5.3 (1.0) 192 5.3 (1.0) −0.3 −0.5,0.01 0.053
Men 159 5.3 (1.0) 148 5.4 (0.9) 164 5.4 (1.1) 149 5.5 (1.0) −0.05 −0.35, 0.26 0.77
Open in a new tab

DiD = Mean difference-in-difference. High attendance defined as attendance in at least 6 of 8 agricultural trainings and 1 of 2 financial trainings.

The intervention improved both women’s and men’s crop diversity scores (women 1.7 [1.19, 2.26]; men 2.2 [1.57, 2.77]) but did not affect diet diversity of men or women.

Discussion

We estimated the impacts of Shamba Maisha intervention on agriculture, diets, and nutrition outcomes in rural Kenya. The intervention improved agricultural production diversity and value of agricultural production, but did not improve diet diversity or MUAC. The effect sizes for crop and diet diversity were similar for both men and women, and across seasons.

We posit that the effects on agricultural outcomes across seasons are explained by Shamba Maisha’s focus on a combination of climate-smart practices, including irrigation, protection against excessive rain and floods, climate-adapted seed selection, diversification, and soil management. Climate-smart agriculture practices have improved agricultural outcomes in general populations across diverse settings (including India, Vietnam, California, [29], Kenya [30] Ethiopia [31], and Ghana [32–36]). The combined effects of increased crop value and reduced farmland use indicates that agricultural practices may have become more efficient – an important consideration for rural Kenya’s growing population [37,38]. Agricultural intensification is recommended to reduce land use for agirculture as a key climate mitigation strategy [37,38], and is also important for people with HIV who may have reduced work capacity.

Crop diversity improved for men and women, suggesting the intervention was relevant and inclusive of women. Studies in Ethiopia [33], Zambia, and Malawi [39] found women experienced barriers to uptake of climate-smart agriculture, and male-headed households were more likely to adopt climate-smart agricultural practices [31,33] due to men’s greater access to land, capital, and information. Shamba Maisha may have overcome some gender barriers by ensuring that the promoted practices were relavant and applicable for men and women.

The intervention did not impact diet quality or MUAC, indicating a possible need to incorporate nutrition-related behaviour change. When nutrition is a major focus, agriculture interventions with a clear nutrition focus, i.e. nutrition-sensitive agriculture, consistently improve dietary diversity but not anthropometric status in general propulations [40], indicating that anthropometry can be harder to change. Low MUAC was not very prevalent in our sample, so it may have been difficult to improve.

Strengths and Limitations

The trial design, balance between arms, low attrition, high fidelity and participation levels, season-specific information, and inclusion of both men and women were study strengths. The 24-month study included severe weather events including flooding in rainy seasons and drought in dry seasons of 2018, enabling application of various climate-smart components. Since we used an abbreviated food frequency questionnaire to measure diets, and since changes in dietary patterns take time, our results may be an underestimate of dietary changes. Data on portion sizes and energy expenditure would enable better estimation of nutritional adequacy.

Conclusions

Taken with the main trial findings showing improved food security [15], we conclude that well-designed climate-smart agriculture could be an important means to improve food security and agricultural productivity of both women and men farmers with HIV. Further nutrition-related behaviour change may be needed to improve nutrition outcomes [41,42].

Integration of climate-smart agricultural interventions could be done by expanding existing nutrition, counselling and support services typically provided to people with HIV, and by scaling up through agriculture extension. Frameworks and political commitment exist for both in Kenya. Models developed to operationalize the integration of nutritional support into HIV care can be used similarly to integrate climate-smart agriculture. The Kenyan government has expressed commitment to addressing climate risks, through cross-cutting strategies to build climate adaptation and food security [30]. This study demonstrates that collaborations can be established between HIV health programs and the agricultural sector to improve agriculture-based livelihoods of people living with HIV.

Supplementary Material

Supplemental Data File (.doc, .tif, pdf, etc.)

Acknowledgements

We are grateful to the Kenyan men and women who generously contributed their time by participating in this study. We thank the Kenyan Medical Research Institute (KEMRI), the University of California San Francisco (UCSF), and the UCSF Global Programs for Research and Training for their important support. We would also like to recognize the Director General of KEMRI, the Director of KEMRI’S Center for Microbiology Research, and Kisumu, Homa Bay, and Migori County Ministries of Health for their support of this research. We also thank Pauline Wekesa, Bernard Rono, Brian Polo, Phoebe Olugo, Sylvia Atieno, Maureen Nyaura, Sylvia Akoko, Titus Arunga, Belinda Odhiambo, Richard Omondi, Elly Bwana, Emmanuel Otieno, Julie Omoro, Doreen Otieno, Rose Ngwengi, Amos Onyango, Sharon, Owour, Pius Atonga, Fredrick Ouko, Nicholas Ambira, George Kennedy, Geoffery Ojuok, Risper Omollo, Elija Mbaja, Valiant Odhiambo, Peter Obando, and Julias Odhacha for their important contributions to this research.

Sources of support:

The research described was supported by grant 1R01MH107330 from the National Institute of Mental Health, National Institute of Health. Funding of author HHF was provided by Wellcome Trust, Grant/Award Number: 210894/Z/18/Z.

Footnotes

Disclaimers: All authors declare no conflicts of interest.

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Supplementary Materials

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