Living Standards Measurement Study

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The Living Standards Measurement Study (LSMS) team was a pioneer recognizing the lack of high-quality agricultural data, as well as the need of its timely availability more than 15 years ago. Against this background, it created the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) project, with the objective of designing and implementing systems of multi-topic, nationally representative panel household surveys with a strong focus on agriculture. 

The work was done in partnership with the national statistical offices of eight countries in Sub-Saharan Africa – Burkina Faso, Ethiopia, Malawi, Mali, Niger, Nigeria, Tanzania and Uganda. A key element of the LSMS-ISA was the launch of an extensive program of methodological research aimed at developing and validating methods for improved agricultural data collection. 

The expertise accumulated over the years by the LSMS team is invaluable. Through the implementation of methodological experiments, it has enhanced the quality and efficiency of agricultural data collected in household surveys. With an emphasis on the integration of objective measurements, our methodological research has tackled numerous themes central to understanding the role of agriculture in livelihoods and levers to increasing productivity. In evolving agricultural and technological landscapes, research undertaken by the LSMS, aimed at integrating surveys and satellites, points to the fact that agricultural household surveys are not a thing of the past.  

Key themes of LSMS agriculture-related methods research are highlighted in the section below, but for an overview on agricultural survey design, see the LSMS Guidebook on Agricultural Survey Design. And for recent work on the use of phone surveys for agricultural data collection, see From Necessity to Opportunity: Lessons for Integrating Phone and In-Person Data Collection for Agricultural Statistics in a Post-Pandemic World. 

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1.      Measuring Agricultural Land Area

2.      Measuring Individual Land Tenure Rights in the Sustainable Development Goals (SDGs) Agenda  

3.      Testing Innovations for Improved Data on Soil Health  

4.      Measuring Crop Production  

5.      Measuring Crop Losses  

6.      Identifying Crop Varieties 

7.      Measuring Labor in Agriculture 

8.      Measuring Production in the Livestock Sector

9.      Measuring Access to and Use of Digital Farmer Services

10.    Capturing Activities in Other Farm-Related Sectors: Fisheries & Forestry

11.    Improving Local Accuracy of Remote Sensing Climate Data

12.    Integrating Surveys & Satellites

1. Measuring Agricultural Land Area 

Land area measurement is the cornerstone of agricultural statistics and analysis, with land being arguably the most important productive asset for rural households across developing regions. Randomized survey experiments conducted by the LSMS team evaluated different approaches to land area measurement in smallholder production systems - including farmer estimates of area, use of the compass and rope method, and the use of handheld GPS units – and have showcased systematic measurement error in farmer reporting.

They have provided robust empirical evidence in support of the adoption of handheld GPS-based plot area measurement in large-scale household and farm surveys. The results from these studies, as well as analysis of national LSMS-ISA data, have fed into the LSMS Guidebook on Land Area Measurement. Ongoing methodological research efforts are now assessing the feasibility and accuracy of farmers’ identification of plots from high-resolution satellite imagery, as well as the accuracy of GPS-based area measurements taken directly with CAPI tablets, such as through the Survey Solutions feature for area measurement.

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  • Assessing Land Area from Above: Evidence from Armenia (forthcoming) 

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2. Measuring Individual Land Tenure Rights in the Sustainable Development Goals (SDGs) Agenda 

Recognition of the importance of land as a key resource has driven demand for strengthening individual tenure security for all and prompted the inclusion of Indicators 1.4.2 and 5.a.1 in the SDG agenda. The LSMS team collaborated with the custodians of these SDG indicators, including UN-Habitat, the World Bank, and FAO, and with the Global Donor Working Group on Land and the Global Land Indicators Initiative, to design a standardized and succinct survey instrument to collect the essential data for computation of both indicators simultaneously.  

The joint  questionnaire modules and accompanying guidance note  aims to facilitate the successful, efficient, and cross-country comparable data collection for computation of SDG indicators 1.4.2 and 5.a.1. The modules, which were designed to fit various survey designs and with different respondent strategies, were tested in Armenia as part of the Armenia Land Tenure and Area (ALTA study) and adopted in LSMS-ISA surveys.

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3. Testing Innovations for Improved Data on Soil Health 

The importance of soil health in agricultural activities is unquestionable, yet the complex nature of soil makes it much more challenging to measure than other agricultural inputs. Historically, household surveys have either included subjective questions of farmer assessment or relied on national-level soil maps to control for land quality, if anything at all. Recent scientific advances in laboratory soil analysis—via spectral soil testing—have opened the door to more rapid, cost-effective objective measurement of soil health in household surveys.  

Randomized survey experiments led by the LSMS team in Ethiopia and Uganda have tested the relative accuracy and cost effectiveness of soil fertility assessment based on farmer reporting and infrared spectroscopy-based lab analyses vis-à-vis wet chemistry—the gold standard in soil science. The results from these experiments have informed our guidebook on integrating  Spectral Soil Analysis in Household Surveys. Ongoing methodological efforts are now aimed at assessing the feasibility and accuracy of handheld spectrometers for in-situ soil health assessments, as well as the potential for imputation approaches to offset scalability challenges in the implementation of objective soil health measurement. 

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4. Measuring Crop Production 

Despite the importance of the agricultural sector in reducing poverty and food insecurity, serious weaknesses in agricultural statistics persist. Difficulties in properly estimating crop productivity in particular are compounded by several features, such as the length and frequency of harvesting for certain types of extended harvest crops - like cassava - the length of the recall period with which agricultural production is often asked, the various non-standard units in which production is reported, and rounding and other sources of error in respondent-reported production.  

Consequently, the LSMS team has set out to validate different approaches to collecting data on crop production, including through crop-cutting and the potential combination with imputation approaches, to understand the implications of recall periods on the quality of respondent-reported production, and to provide guidance on the collection and use of non-standard units for production measurement.  

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5. Measuring Crop Losses 

Crop losses can happen at various stages of the production process. They encompass losses that occur both at harvest and post-harvest and constitute an integral element in the determination of food losses and, consequently, the actual food supply. From an economic viewpoint, crop losses often represent foregone revenues for the household and, more in general, they constitute a threat to the lives and livelihoods of those depending on agriculture.

With climate change accelerating, and more frequent and severe disasters negatively impacting production, crop losses are of increasing concern, especially in low- and middle-income countries (LMICs) where, every year, they translate in billions of dollars of economic losses. Despite that, accurately measuring crop losses remains extremely challenging due to a variety of factors such as differences in terms of crop types, originating causes, time of realization, agroecological conditions and management practices.  

The LSMS team, in part under the 50x2030 Initiative, has been researching innovative methods to improve the estimation of on-farm crop losses based on household and agricultural survey data, and their attribution to climate change-related disasters. Results thus far indicate potential for the adoption of model-based approaches combined with sub-sampling for the collection of detailed crop loss information in household surveys, as well as the need to further invest in the enhanced design of crop losses related questions and investigate avenues for integrating high frequency data collections through phone surveys, in situ and remote sensor-based technologies. 

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6. Identifying Crop Varieties

Accurate crop varietal identification is essential for assessing crop performance, but also to inform distribution and density of specific varieties with enhanced nutritional and/or productivity traits, which is of strong interest to agricultural research centers and local institutions. Yet, this data is still lacking at national scale in most low- and middle-income countries (LMICs), mainly due to substantial technical and economic challenges related to their collection.  

The LSMS team, in collaboration with other national and international partners such as the CGIAR Standing Panel on Impact Assessment (SPIA), has been pioneering the use of DNA fingerprinting in Malawi, Ethiopia and Uganda as the benchmark method for collecting crop varietal data in household surveys. This has spurred methodological research aimed at validating different survey-based approaches for the collection of high-quality crop varietal data and their potential for large-scale implementation.  

In Ethiopia, the LSMS team assessed the relative accuracy of three subjective methods of identifying sweet potato varietal adoption – including elicitation from farmers with basic questions or visual-aids, and enumerator recording observations – against the benchmark method. Results show scarce reliability for all three methods, with visual aid protocols offering better results compared to basic survey questions. In Uganda, the LSMS team validated the use of hyperspectral sensors for collecting in-situ measures of banana plant varieties and the potential to extrapolate these measures to regional scale through the integration of high-resolution satellite imagery. Initial results for this approach are encouraging and call for further research to better understand the key influencing factors of spectral responses. 

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7. Measuring Labor in Agriculture 

In low-income countries, a large share of labor is in the form of work on the household farm. The traditional approach of collecting data on agricultural labor is through recall methods, collecting the accumulated total number of days/hours a person worked on the farm over the season. There is a general perception that the data, collected by long periods of recall over which the household must make a difficult mental calculation, is fraught with measurement error.  

This measurement error may be different or affect certain populations more than others (e.g. by gender, income level, or type of farming and crop). Moreover, the details of the work or its intensity over the agricultural season are rarely known. These issues have been explored in a series of methodological experiments (in Tanzania, Ghana and Malawi) to measure and compare the impact of different methods of collecting household agricultural labor information. The goals are twofold: to assess the accuracy of the traditional recall surveys and to explore the option of mobile phone updates as an intermediate approach.   

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Journal Articles

·        Measuring Farm Labor: Survey Experimental Evidence from Ghana

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8. Measuring Production in the Livestock Sector 

Livestock production is an important contributor to rural livelihoods in developing countries and is often a key source of nutritious food, income, and draft power. An estimated 600 million poor small farmers earn their livelihoods from livestock, and the already significant demand for livestock products is expected to grow by as much as 50 percent by 2050 (FAO, 2020). High quality and timely data on livestock are essential to understanding the role of livestock in household welfare, the contributions of the sector to food systems and nutrition, and its interactions with climate change.  

Building on past work undertaken by the LSMS team in collaboration with FAO, including the publication of a guidebook on collecting livestock data in multi-topic household surveys, the LSMS is currently undertaking methodological research aimed at improving the quality and cost-effectiveness of data on livestock counts, quantification, and production by leveraging high-frequency data collection approaches and integrating technological innovations. 

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9. Measuring Access to and Use of Digital Farmer Services

Digital Farmer Services (DFS) play a critical role in modernizing agriculture, empowering farmers with tools to improve productivity, market access, and resilience. In recent years, financing to DFS in developing countries have surged with increasing investments made by development actors in initiatives such as mobile-based advisory services, precision agriculture tools, digital credit and marketplaces. Yet data on penetration of digital technologies, let alone on access and use of digital solutions for farming, is still very limited and not systematically collected in national surveys.  

This data is essential to monitor diffusion and agricultural trends and to provide tailored solutions, fostering data-driven decision making. Under the 50x2030 Initiative, the LSMS team has been collaborating with 60 Decibels to develop core questionnaire modules on DFS that are anchored in and aligned with best practices and methodologies for integration into national agricultural household surveys. The Nigeria General Household Survey, Panel 2023-2024 Wave 5, includes modules on farmers access, use, and experience with Digital Farming Services. More work is underway to produce a condensed DFS survey instrument building on the Nigeria work.  

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10. Capturing Activities in Other Farm-Related Sectors: Fisheries & Forestry

Rural economies encompass more than crop and livestock production, with sectors such as fisheries and forestry playing vital roles in agricultural systems. These sectors provide essential income, employment opportunities, food security, and ecosystem services for households and communities. They also contribute significantly to gender and social inclusion.

However, collecting data on fisheries and forestry in household surveys presents unique challenges due to the seasonal and informal nature of these activities, as well as the difficulty in accurately estimating the quantities of products harvested or caught.

The LSMS team has contributed to set standards and best practices for the collection of fisheries and forestry data through the development of survey instruments and guidelines and their integration into national surveys. This work has advanced global statistics on the values, products, and services derived from forests, trees, and fisheries, providing deeper insights into their role in supporting household livelihoods. 

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11. Improving Local Accuracy of Remote Sensing Climate Data

Accurate measurement of weather and climate is crucial for understanding household welfare, as these factors directly influence livelihoods, health, and economic stability. Reliable weather and climate data is also essential for precise local forecasting and improved planning and decision making. To inform policies and interventions that are effective at building adaptation and resilience locally, these data must be integrated with granular agricultural and socioeconomic data. This is done using a variety of methods and data products, but its suitability for micro-level analyses is not fully understood.  

The LSMS team is filling this knowledge gap by conducting methodological research to evaluate measurement error and develop approaches for increasing the reliability of survey-integrated weather and climate data products. Recent research undertaken by the LSMS team, in collaboration with the University of Arizona, highlights that survey-based estimates of agricultural productivity are significantly affected by the choice of remote sensing weather datasets.  

However, further analysis is needed, particularly using a credible ground truth benchmark - an element often lacking in developing regions. To address this gap, ongoing methodological efforts are focused on testing and validating the deployment of in-situ weather sensors within survey communities. These efforts aim to establish reliable benchmarks in areas with sparse and inconsistent weather station coverage, thereby advancing this line of research. 

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12. Integrating Surveys & Satellites 

Collecting GPS coordinates or plot boundaries as part of household survey data collection unlocks additional value, through enabling the integration of survey data with satellite-based remote sensing data.  This can be in the form of integrating household or plot coordinates with various geospatial datasets, such as rainfall, temperature, or land cover to extract additional information at those locations, or more advanced applications.  

The LSMS team has been undertaking a line of research, in part under the 50x2030 Initiative, to identify the potential for the integration of survey and satellite data for crop type and area mapping at scale, as well as the best practices in survey data collection to enable those applications. Evidence to date highlights the need for georeferenced survey data with information about crop types, yields, and agricultural practices to serve as an input to these applications in order to calibrate and validate estimation models. 

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