We describe changes in the cropland distribution for physiographic and bioregions of continental Ecuador between 2000 and 2016 using Landsat satellite data and government statistics. The cloudy conditions in Ecuador are a major constraint to satellite data analysis. We developed a two-stage cloud filtering algorithm to create cloud-free multi-temporal Landsat composites that were used in a Random Forest model to identify cropland. The overall accuracy of the model was 78% for the Coast region, 86% for the Andes, and 98% for the Amazon region. Cropland density was highest in the coastal lowlands and in the Andes between 2500 and 4400 m. During this period, cropland expansion was most pronounced in the Páramo, Chocó Tropical Rainforests, and Western Montane bioregions. There was no cropland expansion detected in the Eastern Foothill forests bioregion. The satellite data analysis further showed a small contraction of cropland (4%) in the Coast physiographic region, and cropland expansion in the Andes region (15%), especially above 3500m, and in the Amazon region (57%) between 2000 and 2016. The government data showed a similar contraction for the Coast (7%) but, in contrast with the satellite data, they showed a large agricultural contraction in the Andes (39%) and Amazon (50%). While the satellite data may be better at estimating relative change (trends), the government data may provide more accurate absolute numbers in some regions, especially the Amazon because separating pasture and tree crops from forest with satellite data is challenging. These discrepancies illustrate the need for careful evaluation and comparison of data from different sources when analyzing land use change.

Obtaining detailed data on the spatio-temporal variation in crop phenology is critical to increasing our understanding of agro-ecosystem function, such as their response to weather variation and climate change. It is challenging to collect such data over large areas through field observations. The use of satellite remote sensing data has made phenology data collection easier, although the quality and the utility of such data to understand agro-ecosystem function have not been widely studied. Here, we evaluated satellite data-based estimates of rice phenological stages in California, USA by comparing them with survey data and with predictions by a temperature-driven phenology model. We then used the satellite data-based estimates to quantify the crop phenological response to changes in weather. We used time-series of MODIS satellite data and PhenoRice, a rule-based rice phenology detection algorithm, to determine annual planting, heading and harvest dates of paddy rice in California between 2002 and 2017. At the state level, our satellite-based estimates of rice phenology were very similar to the official survey data, particularly for planting and harvest dates (RMSE = 3.8-4.0 days). Satellite based observations were also similar to predictions by the DD10 temperature-driven phenology model. We analyzed how the timing of these phenological stages varied with concurrent temperature and precipitation over this 16-year time period. We found that planting was earlier in warm springs (-1.4 days °C-1 for mean temperature between mid-April and mid-May) and later in wet years (5.3 days 100 mm-1 for total precipitation from March to April). Higher mean temperature during the pre-heading period of the growing season advanced heading by 2.9 days °C-1 and shortened duration from planting to heading by 1.9 days °C-1. The entire growing season was reduced by 3.2 days °C-1 because of the increased temperature during the rice season. Our findings confirm that satellite data can be an effective way to estimate variations in rice phenology and can provide critical information that can be used to improve understanding of agricultural responses to weather variation.

Declines in agricultural biodiversity associated with modern farming practices may negatively affect the sustainability of agro-ecosystems, but formal knowledge of historical variation in spatio-temporal variation of agro-biodiversity is limited. We used time series of national (1947-2014) and district-level (1956-2008) crop distribution data for India to show that despite strong agricultural intensification after 1960, the average crop species diversity at the district level was stable, but increased at the country-level. While there was a decline in diversity in the major rice and wheat producing regions of northwestern India, associated with intensification of the production of these crops, diversity in western and southern India increased due to expansion of oilseeds and horticultural crops that replaced millet and sorghum. These opposite, but related, trends in crop-level diversity at the sub-national level partially canceled each other out at national level, but there nevertheless was a noticeable increase in overall crop diversity in India during this time period. Our results illustrate how patterns of change in crop diversity need to be considered at different levels of aggregation, and how a decrease in diversity associated with intensification and specialization in one area, may be associated with increased diversity elsewhere, and that support for intensive agriculture with relatively low crop diversity in some regions may be associated with an increase in crop diversity in other regions and at a higher level of aggregation.

Agricultural index insurance contracts increasingly use remote sensing data to estimate losses and determine indemnity payouts. Index insurance contracts inevitably make errors, failing to detect losses that occur and issuing payments when no losses occur. The quality of these contracts and the indices on which they are based, need to be evaluated to assess their fitness as insurance, and to provide a guide to choosing the index that best protects the insured. In the remote sensing literature, indices are often evaluated with generic model evaluation statistics such as R2 or Root Mean Square Error that do not directly consider the effect of errors on the quality of the insurance contract. Economic analysis suggests using measures that capture the impact of insurance on the expected economic well-being of the insured. To bridge the gap between the remote sensing and economic perspectives, we adopt a standard economic measure of expected well-being and transform it into a Relative Insurance Benefit (RIB) metric. RIB expresses the welfare benefits derived from an index insurance contract relative to a hypothetical contract that perfectly measures losses. RIB takes on its maximal value of one when the index contract offers the same economic benefits as the perfect contract. When it achieves none of the benefits of insurance it takes on a value of zero, and becomes negative if the contract leaves the insured worse off than having no insurance. Part of our contribution is to decompose this economic well-being measure into an asymmetric loss function. We also argue that the expected well-being measure we use has advantages over other economic measures for the normative purpose of insurance quality ascertainment. Finally, we illustrate the use of the RIB measure with a case study of potential livestock insurance contracts in Northern Kenya. We compared 24 indices that were made with 4 different statistical models and 3 remote sensing data sources. RIB for these indices ranged from 0.09 to 0.5, and R2 ranged from 0.2 to 0.51. While RIB and R2 were correlated, the model with the highest RIB did not have the highest R2. Our findings suggest that, when designing and evaluating an index insurance program, it is useful to separately consider the quality of a remote sensing-based index with a metric like the RIB instead of a generic goodness-of-fit metric.

The reduced emissions in deforestation and degradation (REDD+) initiative uses payments for ecosystem services as incentives for developing countries to manage and protect their forests. REDD+ initiatives also prioritize social (and environmental) co-benefits aimed at improving the livelihoods of communities that are dependent on forests. Despite the incorporation of co-benefits into REDD+ goals, carbon sequestration remains the primary metric for which countries can receive payments from REDD+, but after more than 10 years of REDD+, many site-specific programs have failed to complete the carbon verification process. Here, we examine whether the REDD+ social co-benefits alone are sufficient to have slowed deforestation in the absence of carbon payments on Pemba, Tanzania. Using satellite imagery (Landsat archive), we quantified forest cover change for the period before (2001-2010) and after (2010-2018) the launch in 2010-2011 of Pemba island's REDD+ readiness project. We then compared rates of forest cover change between shehia (administrative units) that were part of REDD+ readiness intervention and those that were not, adjusting for confounding variables and the non-random selection of REDD+ shehia with a statistical matching procedure. Despite considerable variation in forest outcomes among shehia, the associated co-benefits with the Pemba REDD+ project had no discernible effect on forest cover change. Likewise, we did not detect an effect of socioecological covariates on forest cover change across all shehia, though island-wide human population growth since 2012 may have played a role. These findings are unsurprising given the failure to secure carbon payments on Pemba and indicate that co-benefits alone are insufficient to reduce deforestation. We conclude that better oversight of all-involved parties is needed to ensure that REDD+ interventions satisfactorily conclude the process of securing a mechanism for carbon payments, if slowing deforestation is to be achieved.