Search

An accurate understanding of how input use affects agricultural productivity in smallholder farming systems is key to designing policies that can improve productivity, food security, and living standards in rural areas. Studies examining the relationships between agricultural productivity and inputs typically rely on land productivity measures, such as crop yields, that are informed by self-reported survey data on crop production. This paper leverages unique survey data from Mali to demonstrate that self-reported crop yields, vis-à-vis (objective) crop cut yields, are subject to non-classical measurement error that in turn biases the estimates of returns to inputs, including land, labor, fertilizer, and seeds. The analysis validates an alternative approach to estimate the relationship between crop yields and agricultural inputs using large-scale surveys, namely a within-survey imputation exercise that derives predicted, otherwise unobserved, objective crop yields that stem from a machine learning model that is estimated with a random subsample of plots for which crop cutting and self-reported yields are both available. Using data from a methodological survey experiment and a nationally representative survey conducted in Mali, the analysis demonstrates that it is possible to obtain predicted objective sorghum yields with attenuated non-classical measurement error, resulting in a less biased assessment of the relationship between yields and agricultural inputs. The discussion expands on the implications of the findings for (i) future research on agricultural intensification, and (ii) the design of future surveys in which objective data collection could be limited to a subsample to save costs, with the intention to apply the suggested machine learning approach.

Department of Energy Engineering, Central University of Jharkhand, Brambe-835 205, Jharkhand, India Department of Mechanical Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj-211 004, Uttar Pradesh, India E-mail: itswaytoabhay@gmail.com, rahuldsurya@mnnit.ac.in, sanjoy.samdarshi@cuj.ac.in Manuscript received online 10 July 2020, revised and accepted 31 October 2020 The research aims to analyse the performance of solar energy operated crop cutting machine (SEOCCM). During the cutting of crops, farmers face harsh climatic conditions such as summer (for cutting crops of wheat, sugarcane, corn, mustard, etc.), and winter (for cutting crops of rice, sunflower, herbs, etc). It is a general observation that to avoid such harsh conditions of weather, the farmer used to cut the crop during the night (in summer especially) and sometimes face accidents such as snakebite, insect bite, or wild animal’s attacks which lead to the death of several farmers. As manual cutting (i.e. utilising by manpower) takes several hours, sometimes a farmer loses his crop due to a sudden change in weather such as rain. There are various machines available such as harvester which consumes either conventional fuel (Diesel) or electricity which leads to air pollution. Hence, there is a requirement of a machine which consumes no conventional energy, saves both times, and cost. To get rid of all, SEOCCM is conceptualized for providing a solution to the farmer who cut the crop manually or using a conventional source of energy. It is fabricated using a PV module (165Wp), DC motor (250W, 12V, 4.2A, 1500 rpm), cutter (diameter of 250 mm, tips of Tungsten Carbide), charge controller, battery (80Ah) and iron frame. SEOCCM saves a conventional source of energy (e.g. fuel, or electricity) as it works on solar energy which is a renewable form of energy. The other features of SEOCCM include: (i) provisions for water bottle, (ii) meal, (iii) umbrella, (iv) LED light, (v) Insect rappeler (by ultrasonic sound waves). In the performance analysis of SEOCCM, various climatic (solar radiation (global, diffuse and 'on tilted surface'), temperature (ambient, PV module surfaces), load i.e. DC Motor) and operational parameters (output parameters such as voltages and currents of PV, Battery and load) are observed in the summer (June 2019) condition of Prayagraj (Old Name: Allahabad, Latitude 25°27´, Longitude 81°44´), Uttar Pradesh, India. It is found that average solar radiation in the month of June in Prayagraj is 850W/m2 which generates the voltage from PV (mounted with an inclination of 25°) of 14V and gives the power of 76W at surface temperatures on top and bottom ranges from 47° C to 50°C and 55°C to 58°C respectively, average wind speed of 3.5 m/s and the maximum ambient temperature of 48.9°C. And load consumes on average 60W.&nbsp

This paper revisits the decades-old puzzle of the inverse plot-size productivity relationship, which states that land productivity decreases as plot size increases. Existing empirical studies on the inverse plot-size productivity relationship define land productivity or yields as self-reported production divided by plot size. This paper considers an alternative approach to estimating yields based on crop cuts. The crop-cut method entails measuring and harvesting randomly selected subplots by trained technicians, and is recommended by the Food and Agriculture Organization for the accurate measurement of crop production. Using data representative of rural Ethiopia, the analysis indicates that the inverse relationship is strong when based on self-reported production, but disappears when based on crop-cut estimates. The inference from these findings is that the inverse relationship is an artifact of systematic overreporting of production by farmers on small plots, and underreporting on larger plots. The paper also discusses how rejecting the inverse plot-size productivity relationship has significant implications for the inverse farm-size productivity relationship.

The advent of multiple satellite systems capable of resolving smallholder agricultural plots raises possibilities for significant advances in measuring and understanding agricultural productivity in smallholder systems. However, since only imperfect yield data are typically available for model training and validation, assessing the accuracy of satellite-based estimates remains a central challenge. Leveraging a survey experiment in Mali, this study uses plot-level sorghum yield estimates, based on farmer reporting and crop cutting, to construct and evaluate estimates from three satellite-based sensors. Consistent with prior work, the analysis indicates low correlation between the ground-based yield measures (r = 0.33). Satellite greenness, as measured by the growing season peak value of the green chlorophyll vegetation index from Sentinel-2, correlates much more strongly with crop cut (r = 0.48) than with self-reported (r = 0.22) yields. Given the inevitable limitations of ground-based measures, the paper reports the results from the regressions of self-reported, crop cut, and (crop cut-calibrated) satellite sorghum yields. The regression covariates explain more than twice as much variation in calibrated satellite yields (R2 = 0.25) compared to self-reported or crop cut yields, suggesting that a satellite-based approach anchored in crop cuts can be used to track sorghum yields as well or perhaps better than traditional measures. Finally, the paper gauges the sensitivity of yield predictions to the use of Sentinel-2 versus higher-resolution imagery from Planetscope and DigitalGlobe. All three sensors exhibit similar performance, suggesting little gains from finer resolutions in this system.

Crop yields in smallholder systems are traditionally assessed using farmer-reported information in surveys, occasionally by crop cuts for a sub-section of a farmer's plot, and rarely using full-plot harvests. Accuracy and cost vary dramatically across methods. In parallel, satellite data is improving in terms of spatial, temporal, and spectral resolution needed to discern performance on smallholder plots. This study uses data from a survey experiment in Uganda, and evaluates the accuracy of Sentinel-2 imagery-based, remotely-sensed plot-level maize yields with respect to ground-based measures relying on farmer self-reporting, sub-plot crop cutting (CC), and full-plot crop cutting (FP). Remotely-sensed yields include two versions calibrated to FP and CC yields (calibrated), and an alternative based on crop model simulations, using no ground data (uncalibrated). On the ground, self-reported yields explained less than 1 percent of FP (and CC) yield variability, and while the average difference between CC and FP yields was not significant, CC yields captured one-quarter of FP yield variability. With satellite data, both calibrated and uncalibrated yields captured FP yield variability on pure stand plots similarly well, and both captured half of FP yield variability on pure stand plots above 0.10 hectare. The uncalibrated yields were consistently 1 ton per hectare higher than FP or CC yields, and the satellite-based yields were less well correlated with the ground-based measures on intercropped plots compared with pure stand ones. Importantly, regressions using CC, FP and remotely-sensed yields as dependent variables all produced very similar coefficients for yield response to production factors.

Despite a growing interest in using satellite data to estimate paddy rice yield in Southeast Asia, significant cloud coverage has led to a scarcity of usable optical data for such analysis. In this paper, we study the feasibility of using two alternative sources of satellite data - (i) surface reflectance fusion data which integrates Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) images, and (ii) L-band radar backscatter data from the Advanced Land Observing Satellite 2 (ALOS-2) PALSAR-2 sensors - to circumvent the cloud cover problem and estimate yield in Thai Binh Province, Viet Nam during the second growing season of 2015. Our findings indicate that although Landsat - MODIS fusion data are not necessarily beneficial for paddy rice mapping when compared with only using Landsat data, fusion data allows us to estimate the peak value of various vegetation indexes and derive the best empirical relationship between these indexes and yield data from the field. We also find that the L-band radar data not only has a lower performance in paddy rice mapping when compared with optical data, but also contributes little to rice yield estimation.

Based on a two-round household panel survey conducted in Eastern Uganda, this study shows that the analysis of the inverse scale-productivity relationship is highly sensitive to how plot-level maize production, hence yield (production divided by GPS-based plot area), is measured. Although farmer-reported production-based plot-level maize yield regressions consistently lend support to the inverse scale-productivity relationship, the comparable regressions estimated with maize yields based on sub-plot crop cutting, full-plot crop cutting, and remote sensing point toward constant returns to scale, at the mean as well as throughout the distributions of objective measures of maize yield. In deriving the much-debated coefficient for GPS-based plot area, the maize yield regressions control for objective measures of soil fertility, maize genetic heterogeneity, and edge effects at the plot level; a rich set of plot, household, and plot manager attributes; as well as time-invariant household- and parcel-level unobserved heterogeneity in select specifications that exploit the panel nature of the data. The core finding is driven by persistent overestimation of farmer-reported maize production and yield vis-à-vis their crop cutting–based counterparts, particularly in the lower half of the plot area distribution. Although the results contribute to a larger, and renewed, body of literature questioning the inverse scale-productivity relationship based on omitted explanatory variables or alternative formulations of the agricultural productivity measure, the paper is among the first documenting how the inverse relationship could be a statistical artifact, driven by errors in farmer-reported survey data on crop production.