Agricultural Survey Data Research Articles

Exploring the Relation between Farmer Group Membership and Agricultural Productivity: Evidence from Indonesian Rice Farming

Farmer groups have been critical in pursuing agricultural development, particularly in developing countries. Indonesia’s government has promoted the development of farmer groups in recent years, mainly in the rice sector, as a strategic agricultural commodity. This paper explored the relations between farmer group membership, farming practices, and productivity in the Indonesian rice sector. Propensity Score Matching (PSM) and regression analysis were employed to examine the 2014 National Agricultural Survey data. The empirical findings confirmed the positive relations between farmer group membership and productivity in the Indonesian rice sector. Furthermore, farmers’ characteristics covering age, gender, education level, and farming practices, such as land status and access to irrigation, all played a role in determining the extent to which farmers participated in farmer groups.

Greenhouse farming and employment: Evidence from Ecuador

The transforming agri-food sector is an important contributor to employment generation in rural areas of lower-income countries. However, little attention has been paid to the question of how technology affects the quality and quantity of employment in the sector. In this paper, we provide the first empirical evidence of how the rise in greenhouse farming changes labor demand (i.e., the number of employees), using large-scale and nationally representative agricultural survey data from Ecuador between 2014 and 2021 and pseudo-panel as well as event-study estimation techniques. Contrary to fears that more technologically advanced production methods displace large shares of the workforce, we find that greenhouse farming is associated with higher labor demand. Specifically, greenhouse farms hire more female workers and workers on permanent contracts.

Do women produce efficiently? Empirical evidence from rice farming in Indonesia

Many studies have emphasized the important role of women in the agriculture sector and rural development. However, many existing empirical studies have also shown various barrier faced by women especially due to the limited access and control of agricultural resources. This study aims to analyze the productivity and technical efficiency of female farmers in Indonesian rice industry. To this end, this study using stochastic production function analysis by employing national agricultural survey data in 2014. The empirical results finds that the productivity of female’s farmer in the Indonesian rice farming has been affected by the cultivated land, the use of fertilizer and seed, as well the type of mechanization which propose different marginal productivity for each factor. Meanwhile, the average technical efficiency level is found to be at 0.645 percent. Furthermore, the level of efficiency is explained by the role of the level of education and age of the female farmers, but no effect from the farmer group membership, and participation in the farmers training program.

Post-harvest losses of crops and its determinants in Ethiopia: tobit model analysis

BackgroundReduction of post-harvest loses of crops are crucial tasks in ensuring food and nutrition security. However, a lack of knowledge on the extent of post-harvest losses and its associated impeding factors are posing major challenges to effectiveness of grain post-harvest loss management strategy and to scale up for all other crop types in Ethiopia. The study estimates the magnitude of post-harvest losses of all crops and identifies its determinants.MethodologyA national-level agriculture survey data were obtained from the Central Statistical Agency of Ethiopia. Data were analyzed using descriptive statistics and Tobit model analysis techniques.ResultsThe analysis shows 25.81% perceived annual average post-harvest losses of crops were obtained with considerable variation across the crop types. The main factors impeding post-harvest losses of crops were households with larger adult family size, higher levels of education attendance, and wealth status, large landholding size and damage of stored crops by insect pests infestation and/or rodent feeding due to utilization of traditional storage equipments, access to extension support services, cooperative marketing membership, and being far away from both all weathered roads and near local market centers due to a higher transaction costs associated with a lack of market information, contamination, and mechanical injuries.ConclusionMinimization of post-harvest losses of crops could be achieved through a holistic approach by providing short and long-term training on post-harvest management practices, promoting the use of post-harvest technologies, paying special attention to the institutional support systems (agriculture extension and rural credit services), strengthening the support for post-harvest handling technologies, reinforcing the existing farmer cooperative marketing, and improvements of the local market and road networking infrastructures of rural areas should be undertaken by the government in collaboration with non-government organizations.

Implementable Factors for Sustainable Agricultural Policy in Metro City

Conservation of agricultural land for food sovereignty is one of the SDGs' agenda. In Indonesia, this objective is stated in the Sustainable Food Agricultural Land (LP2B) policy outlined in Law No. 41/2009, which followed by regulations below to the level of the Regional Regulation. In the Metro City, this is stated in Regional Regulation No. 21/2016 to be the basis for a policy of protection of 1,567.5 hectares of paddy fields. This regulation aims to support food sovereignty and maintain the ecological balance of the environment. The problem arises because the paddy fields designated by LP2B are private land so that not all owners are willing to implement this policy. This study aims to analyze the factors that affect the level of willingness of landowners with a quantitative approach by analyzing land data with multiple linear regression analysis techniques. A sample of 513 parcels was taken from the Farmers Owning Farmers population of 6,060 HHs based on Agricultural Survey data, BPS Lampung 2018. In general, from the results of the analysis, the highest coefficient on object characteristics was variable land productivity while the characteristics of subjects were multifunctional knowledge of paddy fields.

Deep machine learning with Sentinel satellite data to map paddy rice production stages across West Java, Indonesia

Indonesia recently implemented a novel, technology-driven approach for conducting agricultural production surveys, which involves monthly observations at many thousands of strategic locations and automated data logging via a cellular phone application. Data from these comprehensive field surveys offer immense value for advancing remote sensing technology to map crop production across Indonesia, particularly through the development of machine learning approaches to relate survey data with satellite imagery. The objective of this study was to compare different machine learning scenarios for classifying and mapping the temporal progression of paddy rice production stages across West Java, Indonesia using synthetic aperture radar (SAR) and optical remote sensing data from Sentinel-1 and Sentinel-2 satellites. Monthly paddy rice survey data at 21,696 locations across West Java from November 2018 through April 2019 were used for model training and testing. Five classes related to rice production stage or other field conditions were defined, including rice at tillering, heading, and harvest stages, rice fields with little to no vegetation present, and non-rice areas. A recurrent neural network (RNN) with long short term memory (LSTM) nodes provided optimal performance with classification accuracies of 79.6% and 75.9% for model training and testing, respectively, and reduced computational effort. Other approaches that incorporated a convolutional neural network (CNN) either reduced classification accuracy or increased computational effort. Deep machine learning methods (RNN and CNN) generally outperformed other non-deep classifiers, which achieved up to 63.3% accuracy for model testing. Classification accuracies were optimized by inputting two Sentinel-1 channels (VH and VV polarizations) and ten Sentinel-2 channels. Temporal patterns of paddy rice production stages were consistent between the monthly ground-based agricultural survey data and 10-m, satellite-based rice classification maps obtained by applying the LSTM-based RNN across West Java. The results demonstrated the value of combining modern agricultural survey data, satellite remote sensing, and a recurrent neural network to develop multitemporal maps of paddy rice production stages.

Mapping Smallholder Maize Farms Using Multi-Temporal Sentinel-1 Data in Support of the Sustainable Development Goals

Reducing food insecurity in developing countries is one of the crucial targets of the Sustainable Development Goals (SDGs). Smallholder farmers play a crucial role in combating food insecurity. However, local planning agencies and governments do not have adequate spatial information on smallholder farmers, and this affects the monitoring of the SDGs. This study utilized Sentinel-1 multi-temporal data to develop a framework for mapping smallholder maize farms and to estimate maize production area as a parameter for supporting the SDGs. We used Principal Component Analysis (PCA) to pixel fuse the multi-temporal data to only three components for each polarization (vertical transmit and vertical receive (VV), vertical transmit and horizontal receive (VH), and VV/VH), which explained more than 70% of the information. The Support Vector Machine (SVM) and Extreme Gradient Boosting (Xgboost) algorithms were used at model-level feature fusion to classify the data. The results show that the adopted strategy of two-stage image fusion was sufficient to map the distribution and estimate production areas for smallholder farms. An overall accuracy of more than 90% for both SVM and Xgboost algorithms was achieved. There was a 3% difference in production area estimation observed between the two algorithms. This framework can be used to generate spatial agricultural information in areas where agricultural survey data are limited and for areas that are affected by cloud coverage. We recommend the use of Sentinel-1 multi-temporal data in conjunction with machine learning algorithms to map smallholder maize farms to support the SDGs.

Characterizing spatiotemporal patterns of crop phenology across North America during 2000–2016 using satellite imagery and agricultural survey data

Crop phenology represents an integrative indicator of climate change and plays a vital role in terrestrial carbon dynamics and sustainable agricultural development. However, spatiotemporal variations of crop phenology remain unclear at large scales. This knowledge gap has hindered our ability to realistically quantify the biogeochemical dynamics in agroecosystems, predict future climate, and make informed decisions for climate change mitigation and adaptation. In this study, we improved an EVI-curve-based approach and used it to detect spatiotemporal patterns in cropping intensity and five major phenological stages over North America during 2000–2016 using vegetation index in combination with agricultural survey data and other ancillary maps. Our predicted crop phenological stages showed strong linear relationships with the survey-based datasets, with R2, RMSEs, and MAEs in the ranges of 0.35 –0.99, three to ten days, and two to eight days, respectively. During the study period, the planting dates were advanced by 0.60 days/year (p < 0.01), and harvesting dates were delayed by 0.78 days/year (p < 0.01) over North America. A minimum temperature increase by 1 °C caused a 4.26-day planting advance (r = −0.50, p < 0. 01) or a 0.66-day harvest delay (r = 0.10, p < 0.01). While, a higher maximum temperature resulted in a planting advance by 4.48 days/°C (r = −0.62, p < 0.01) or a harvest advance by 2.22 days/°C (r = −0.40, p < 0.01). Our analysis illustrated evident spatiotemporal variations in crop phenology in response to climate change and management practices. The derived crop phenological datasets and cropping intensity maps can be used in regional climate assessments and in developing adaptation strategies.

Spatialization of Actual Grain Crop Yield Coupled with Cultivation Systems and Multiple Factors: From Survey Data to Grid

The spatialization of actual grain crop yield helps to understand the spatial heterogeneity of yield and support for the precise farming and targeted farmland management. However, inadequate consideration and quantification of anthropogenic factors affecting the estimation of actual yield distribution easily cause uncertainties in recent researches. Here, we developed a new grain crop yield spatialization (GCYS) model in order to downscale the yield from county to grid scale. The GCYS model is composed of four modules: (a) cultivated land Net Primary Productivity (NPP) calculation module, (b) comprehensive agricultural system construction module, (c) key factors establishment module, and (d) integration and validation module. Its novelty is to realize the actual grain crop yield spatialization from county scale to grid scale by quantifying and spatializing the comprehensive agricultural system when considering the diversity of cultivated structure and field management factors. Taking Guizhou and Guangxi Karst Mountains Region as a study-area, the GCYS model is developed and tested. The determination coefficients of regression analysis between agricultural survey data and spatialization results of paddy rice yield calculated by our model reach 0.72 and 0.76 in 2000 and 2015, respectively (p &lt; 0.01). The results visualize the spatial pattern of actual grain crop yield at the grid scale, which show a gradually decreasing trend from southeast to northwest. With an increase in potential yield and improvement of field management technologies, the actual average yield of grain crops per unit increased form 4745.10 kg/ha of 2000 to 5592.89 kg/ha of 2015. Especially in high-yield zones in southeast area, mechanized cultivation became the dominated factor, rather than chemical fertilizer application. This demonstrates that our model can provide a reference for agricultural resource utilization and policy-making.

Forum: Integration of Crop-Livestock Systems: An Opportunity to Protect Grasslands from Conversion to Cropland in the US Great Plains

The Great Plains is a mixture of cropland and grassland mainly used for agricultural purposes, with grasslands under continual threat of conversion to cropland. Agriculturists are advocating for the integration of crop-livestock systems (ICLS) to recouple nutrient cycles, improve biodiversity, and increase resilience of agricultural operations. We address the benefits of ICLS in the Great Plains, contending that focus on improving soil health and financial stability of agricultural operations should reduce the conversion of grasslands to cropland. Using US Department of Agriculture National Agricultural Statistics Service Census of Agriculture survey data from the 1925−2017 category “cropland used only for pasture or grazing,” which represents land that had been cropped but converted to annual/perennial pasture and grazed, we showcase that the number of farms and the land area in this category is a reasonable proxy of ICLS. As expected, ICLS dramatically decreased in the entire United States from 1925 to 1945, but from 1945 to 2002 in the Great Plains ICLS remained relatively constant, providing evidence of sustained crop-livestock integration. Consistent high numbers of beef cows during this period and the wide availability of forages and crop residues for ruminants facilitated opportunities for producers to use ICLS on their individual operations (within farm) or among operations where row crop farmers and forage-based producers integrated beef cattle use across the landscape (among farms). This integration, however, was decoupled from 2006 to 2013, a period of high grain prices. As a result, economic value of grasslands was decreased and conversion to cropland was increased. Thus, conservation efforts in the Great Plains for grasslands should focus on keeping grasslands intact for provision of multiple ecosystem goods and services by emphasizing incorporation of ICLS within and among farms to reduce the risk of converting grassland to cropland.

Synergistic integration of optical and microwave satellite data for crop yield estimation

Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation (EO) remote sensing data provides a unique source of information to monitor crops in a temporally resolved and spatially explicit way. In this study, we propose the combination of multisensor (optical and microwave) remote sensing data for crop yield estimation and forecasting using two novel approaches. We first propose the lag between Enhanced Vegetation Index (EVI) derived from MODIS and Vegetation Optical Depth (VOD) derived from SMAP as a new joint metric combining the information from the two satellite sensors in a unique feature or descriptor. Our second approach avoids summarizing statistics and uses machine learning to combine full time series of EVI and VOD. This study considers two statistical methods, a regularized linear regression and its nonlinear extension called kernel ridge regression to directly estimate the county-level surveyed total production, as well as individual yields of the major crops grown in the region: corn, soybean and wheat. The study area includes the US Corn Belt, and we use agricultural survey data from the National Agricultural Statistics Service (USDA-NASS) for year 2015 for quantitative assessment. Results show that (1) the proposed EVI-VOD lag metric correlates well with crop yield and outperforms common single-sensor metrics for crop yield estimation; (2) the statistical (machine learning) models working directly with the time series largely improve results compared to previously reported estimations; (3) the combined exploitation of information from the optical and microwave data leads to improved predictions over the use of single sensor approaches with coefficient of determination R≥20.76; (4) when models are used for within-season forecasting with limited time information, crop yield prediction is feasible up to four months before harvest (models reach a plateau in accuracy); and (5) the robustness of the approach is confirmed in a multi-year setting, reaching similar performances than when using single-year data. In conclusion, results confirm the value of using both EVI and VOD at the same time, and the advantage of using automatic machine learning models for crop yield/production estimation.

Predicting high-magnitude, low-frequency crop losses using machine learning: an application to cereal crops in Ethiopia

Timely and accurate agricultural impact assessments for droughts are critical for designing appropriate interventions and policy. These assessments are often ad hoc, late, or spatially imprecise, with reporting at the zonal or regional level. This is problematic as we find substantial variability in losses at the village-level, which is missing when reporting at the zonal level. In this paper, we propose a new data fusion method—combining remotely sensed data with agricultural survey data—that might address these limitations. We apply the method to Ethiopia, which is regularly hit by droughts and is a substantial recipient of ad hoc imported food aid. We then utilize remotely sensed data obtained near mid-season to predict substantial crop losses of greater than or equal to 25% due to drought at the village level for five primary cereal crops. We train machine learning models to predict the likelihood of losses and explore the most influential variables. On independent samples, the models identify substantial drought loss cases with up to 81% accuracy by mid- to late-September. We believe the proposed models could be used to help monitor and predict yields for disaster response teams and policy makers, particularly with further development of the models and integration of soon-to-be available high-resolution, remotely sensed data such as the Harmonized Landsat Sentinel (HLS) data set.

Intensification in agriculture-forest frontiers: Land use responses to development and conservation policies in Brazil

Abstract Deforestation associated with agricultural expansion, particularly that of extensive cattle ranching, remains a pressing challenge for sustainable development and climate mitigation efforts in South America. In response to these challenges, national and local governments, as well as private and non-governmental actors, have developed new forest conservation governance mechanisms. In addition to reducing deforestation for agricultural expansion, it is hoped that these policies may lead to the intensification of existing agricultural regions, thereby contributing to global food availability and continued rural development. The objective of this study is to understand the timing and spatial patterns of crop and pasture intensification in agriculture-forest frontiers in the context of changing conservation policies and rural development. We focus on Mato Grosso, the largest soy and cattle producing state in Brazil, which spans the Cerrado and Amazon biomes and has experienced among the highest levels of deforestation for agricultural expansion globally. Using econometric analysis of remotely sensed and agricultural survey data, we find that cropland and pasture intensification are both linked to increasing forest conservation restrictions and broader supply chain development. However, the effect of conservation restrictions on intensification is lower in regions where there is more forest remaining. While crop and pasture area dynamics are often coupled in agriculture-forest frontiers, crop intensification does not appear to have contributed to pasture intensification through animal feed production. Intensification of crop and pasture areas was associated with temporary, immediate reductions in local deforestation, but crop intensification was associated with increased deforestation over longer time periods. These results suggest that targeted investments in supply chain infrastructure in the Amazon frontier could promote intensification and relieve pressure to clear forests, but must be coupled with substantial, long-term negative incentives for deforestation, including more effective public forest governance and private zero-deforestation commitments.

Uncertainties and implications of applying aggregated data for spatial modelling of atmospheric ammonia emissions

Ammonia emissions vary greatly at a local scale, and effects (eutrophication, acidification) occur primarily close to sources. Therefore it is important that spatially distributed emission estimates are located as accurately as possible. The main source of ammonia emissions is agriculture, and therefore agricultural survey statistics are the most important input data to an ammonia emission inventory alongside per activity estimates of emission potential. In the UK, agricultural statistics are collected at farm level, but are aggregated to parish level, NUTS-3 level or regular grid resolution for distribution to users. In this study, the Modifiable Areal Unit Problem (MAUP), associated with such amalgamation, is investigated in the context of assessing the spatial distribution of ammonia sources for emission inventories.England was used as a test area to study the effects of the MAUP. Agricultural survey data at farm level (point data) were obtained under license and amalgamated to different areal units or zones: regular 1-km, 5-km, 10-km grids and parish level, before they were imported into the emission model. The results of using the survey data at different levels of amalgamation were assessed to estimate the effects of the MAUP on the spatial inventory.The analysis showed that the size and shape of aggregation zones applied to the farm-level agricultural statistics strongly affect the location of the emissions estimated by the model. If the zones are too small, this may result in false emission “hot spots”, i.e., artificially high emission values that are in reality not confined to the zone to which they are allocated. Conversely, if the zones are too large, detail may be lost and emissions smoothed out, which may give a false impression of the spatial patterns and magnitude of emissions in those zones. The results of the study indicate that the MAUP has a significant effect on the location and local magnitude of emissions in spatial inventories where amalgamated, zonal data are used.

Food (In)security and its drivers: insights from trends and opportunities in rural Mozambique

We used multiple rounds of nationally representative agricultural survey data to analyze the trends and drivers of food insecurity in rural Mozambique. Reduced-form Probit models were estimated to explain the observed trends as a function of underlying drivers and factors related to agricultural policy interventions. Despite rapid macroeconomic growth, food insecurity in the rural areas had increased from 42.9 % in 2002 to 47.8 % in 2008. Significant inequalities were also observed in the distribution of food insecurity with a substantial disadvantage to the bottom quintile households and rural households located in the Northern provinces. Limited progress on several drivers of agricultural production and food access as well as geographic disparities appear to explain a significant part of the food insecurity trends and distribution. Whether the indicator was use of improved farm inputs and technology, receipt of agricultural extension services, farm production, or cash income, progress did not occur. This implies that to achieve broad-based food security in rural Mozambique, interventions may need to focus on addressing these drivers to increase agricultural productivity while enhancing resilience to price and weather shocks. Interventions must also be spatially targeted and tailored to each segment of the population.

Evaluation of global land cover maps for cropland area estimation in the conterminous United States

Global land cover data could provide continuously updated cropland acreage and distribution information, which is essential to a wide range of applications over large geographical regions. Cropland area estimates were evaluated in the conterminous USA from four recent global land cover products: MODIS land cover (MODISLC) at 500-m resolution in 2010, GlobCover at 300-m resolution in 2009, FROM-GLC and FROM-GLC-agg at 30-m resolution based on Landsat imagery circa 2010 against the US Department of Agriculture survey data. Ratio estimators derived from the 30-m resolution Cropland Data Layer were applied to MODIS and GlobCover land cover products, which greatly improved the estimation accuracy of MODISLC by enhancing the correlation and decreasing mean deviation (MDev) and RMSE, but were less effective on GlobCover product. We found that, in the USA, the CDL adjusted MODISLC was more suitable for applications that concern about the aggregated county cropland acreage, while FROM-GLC-agg gave the least deviation from the survey at the state level. Correlation between land cover map estimates and survey estimates is significant, but stronger at the state level than at the county level. In regions where most mismatches happen at the county level, MODIS tends to underestimate, whereas MERIS and Landsat images incline to overestimate. Those uncertainties should be taken into consideration in relevant applications. Excluding interannual and seasonal effects, R2 of the FROM-GLC regression model increased from 0.1 to 0.4, and the slope is much closer to one. Our analysis shows that images acquired in growing season are most suitable for Landsat-based cropland mapping in the conterminous USA.

Greenhouse gas mitigation potential from green harvested sugarcane scenarios in São Paulo State, Brazil

Brazil is a major sugarcane producer and São Paulo State cultivates 5.5 million hectares, close to 50% of Brazil's sugarcane area. The rapid increase in production has brought into question the sustainability of biofuels, especially considering the greenhouse gas (GHG) emissions associated to the agricultural sector. Despite the significant progress towards the green harvest practices, 1.67 million hectares were still burned in São Paulo State during the 2011 harvest season. Here an emissions inventory for the life cycle of sugarcane agricultural production is estimated using IPCC methodologies, according to the agriculture survey data and remote sensing database. Our hypothesis is that 1.67 million hectares shall be converted from burned to green harvest scenarios up to years 2021 (rate 1), 2014 (rate 2) or 2029 (rate 3). Those conversions would represent a significant GHG mitigation, ranging from 50.5 to 70.9 megatons of carbon dioxide equivalent (Mt CO2eq) up to 2050, depending on the conversion rate and the green harvest systems adopted: conventional (scenario S1) or conservationist management (scenario S2). We show that a green harvest scenario where crop rotation and reduced soil tillage are practiced has a higher mitigation potential (70.9 Mt CO2eq), which is already practiced in some of the sugarcane areas. Here we support the decision to not just stop burning prior to harvest, but also to consider other better practices in sugarcane areas to have a more sustainable sugarcane based ethanol production in the most dense cultivated sugarcane region in Brazil.

농촌마을 정주환경분석을 통한 과소마을 임계규모 결정지표 개발

ABSTRACT : The rapid rural urban migration and aging has generated an over-depopulation problems in rural areas since the 1980s. The purpose of this study constructs the marginal size of rural over-depopulated village through the analysis of the residential disparities such as farmer’s ratio, basic life service accessibility, and levels of social and economic factors for each village community. This marginal scale could support evaluating diverse rural policies, which have been planned to apply to the rural development programs at the village level. The major challenges for over-depopulated villages are the lack of basic facilities, production infrastructures and inactive communities in the village. Therefore, the quantitative analysis of rural residential disparities according to rural village scale can provide the criteria for rural over-depopulated villages. We utilized Korea Agricultural Survey Data(2010) including specific residential condition of village level. The present study adopt multinomial-logit model for quantitative analysis of different village scales and decomposition techniques to separate the direct effect by the village scale factor from the endowment effects by regional or area characteristics, and residual effect by unknown factors. The present study found that the minimum scale of a rural over-depopulated village was 40 and 60 houses for the respective conditions of farmer’s ratios less than 50% and greater than 75%. It was concluded based on the study findings that threshold scale could support evaluating the diverse rural policies, which have been planned to apply to the rural development programs at the village level.

Dietary Supplement Use in U.S. Army Special Operations Candidates

We administered a dietary supplement survey to 2,215 males (mean age, 25 years; range, 18-47 years) entering U.S. Army Special Forces and Ranger training schools. The survey contained questions on demographics and health as well as the use of vitamin, mineral, protein, pro-performance, and other cofactor supplements; answers were made on a five-point frequency scale. Eighty-five percent of the men reported past or present use of a supplement, 64% reported current use, and 35% reported daily use. Individuals using a supplement at least occasionally were significantly less likely to smoke (p < 0.05) and more likely to exercise on a daily basis. Recent U.S. Department of Agriculture survey data (1994) reported that 39% of males 20 to 39 years old used a dietary supplement at least occasionally. Our data indicate that the rate of supplement use in this study group was much higher than in the general population of young men. This observation supports the need to study more extensively the use and benefits or potential harm of dietary supplementation by otherwise healthy individuals.