Retrospective Predictions of Rice and Other Crop Production in Madagascar Using Soil Moisture and an NDVI-Based Calendar from 2010–2017

Abstract

Malagasy subsistence farmers, who comprise 70% of the nearly 26 million people in Madagascar, often face food insecurity because of unreliable food production systems and adverse crop conditions. The 2020–2021 drought in Madagascar, in particular, is associated with an exceptional food crisis, yet we are unaware of peer-reviewed studies that quantitatively link variations in weather and climate to agricultural outcomes for staple crops in Madagascar. In this study, we use historical data to empirically assess the relationship between soil moisture and food production. Specifically, we focus on major staple crops that form the foundation of Malagasy food systems and nutrition, including rice, which accounts for 46% of the average Malagasy caloric intake, as well as cassava, maize, and sweet potato. Available data associated with survey-based crop statistics constrain our analysis to 2010–2017 across four clusters of Malagasy districts. Strong correlations are observed between remotely sensed soil moisture and rice production, ranging between 0.67 to 0.95 depending on the cluster and choice of crop calendar. Predictions are shown to be statistically significant at the 90% confidence level using bootstrapping techniques, as well as through an out-of-sample prediction framework. Soil moisture also shows skill in predicting cassava, maize, and sweet potato production, but only when the months most vulnerable to water stress are isolated. Additional analyses using more survey data, as well as potentially more-refined crop maps and calendars, will be useful for validating and improving soil-moisture-based predictions of yield.

Reproductive ecology of the black rat (Rattus rattus) in Madagascar: the influence of density‐dependent and ‐independent effects

“…Additionally, increased rainfall during the early rainy season (November-January) was associated with an increase in female maturity and gestation rates during the main breeding season (April-May), particularly in habitats outside of villages and predominantly comprising agricultural land. Madagascar's main growing season begins in November-January (with the onset of the rainy season) and lasts for several months (Rigden et al 2022). Our results therefore suggest that high rainfall at the start of the growing season, when crops are establishing, may provide optimal conditions for rat reproduction later in the rainy season.…”

Can remote sensing enable a Biomass Climate Adaptation Index for agricultural systems?

“…Climatic factors, evapotranspiration, water quality, and topography all have an impact on crop growth (Jia et al, 2020). NDVI can be used to examine crop growth and its relationship with various factors to reveal the important factors for intervention and tracking climate adaptation (Phan et al, 2021;Shen and Evans, 2021;Yadav and Geli, 2021;Rigden et al, 2022). For instance, in China, precipitation was found to be the leading cause of agricultural failure over other factors (Peng et al, 2008), whereas Lamchin et al (2018) found temperature to be the most influential factor in vegetation growth in the Asia region.…”

“…Agricultural yields, such as of tea in Vietnam (Phan et al, 2021), and corn, sorghum, alfalfa, and wheat in New Mexico, USA (Yadav and Geli, 2021) have been predicted by calculating the deviation of historical mean NDVI to the current and assessing the correlation with water stress, extreme weather events, and soil moisture. From time series NDVI (Rigden et al, 2022) and (Wei et al, 2015) have depicted the cropping calendar and explored climatic variability's impact on crop yield (Rigden et al, 2022). Wei et al (2015) define onset and end of growth as the dates when the reconstructed NDVI time-series curve increases and decreases to 20% of the overall level, respectively, and the peak of growth is defined as the date when the reconstructed NDVI time-series curve reaches the maximum.…”

“…Phan et al (2021) use 1 km spatial resolution NDVI derived from MODIS for yield prediction and assessing lag time between growing season and climatic variables for a homogeneous farming system such as tea farming. Similar data have also been used by Rigden et al (2022) to explore the trend of drought impacts on rice, cassava, maize, and sweet potato yield in Madagascar. The impact of climate change on farming systems in Sub-Saharan Africa has also been tracked using an 8 km resolution AVHRR-NDVI (Vrieling et al, 2011).…”

“…Yao et al (2017), for example, were able to estimate wheat leaf area index (LAI) effectively with UAVs narrowband multispectral image (400-800 nm spectral regions, and 10 cm resolution) under varying growth conditions during five critical growth stages, and provide potential technical support for nitrogen fertilization optimization. Satellite data with a wider spectral band and multispectral imagery can help differentiate crop characteristics (i.e., leaves, area) at a stand level (Gnädinger and Schmidhalter, 2017;Jin et al, 2017;Varela et al, 2018), estimate crop yield (Fernandez-Ordonez and Soria-Ruiz, 2017; Yadav and Geli, 2021;Rigden et al, 2022); assess crop health such as pest pressure patterns that cannot be detected by thermal imagery (Khanal et al, 2017), examine soil moisture (Hassan-Esfahani et al, 2017), and crop water stress (Maselli et al, 2020). To overcome the spatial and spectral variations between remote sensing data for developing the CAI, it is important to recognize that satellite data are more likely to be influenced by several factors, including farming system, crop type, growing state, management objectives, and data availability.…”