Knowing where, when, and how much rice is planted and harvested is crucial information for understanding the effects of policy, trade, and global and technological change on food security. We developed RiceAtlas, a spatial database on the seasonal distribution of the world’s rice production. It consists of data on rice planting and harvesting dates by growing season and estimates of monthly production for all rice-producing countries. Sources used for planting and harvesting dates include global and regional databases, national publications, online reports, and expert knowledge. Monthly production data were estimated based on annual or seasonal production statistics, and planting and harvesting dates. RiceAtlas has 2,725 spatial units. Compared with available global crop calendars, RiceAtlas is nearly ten times more spatially detailed and has nearly seven times more spatial units, with at least two seasons of calendar data, making RiceAtlas the most comprehensive and detailed spatial database on rice calendar and production.

Many modern rice varieties (MVs) have been released but only a few have been widely adopted by farmers. To understand farmers’ preferences, we characterized MVs released in the Philippines from 1966 to 2013 and identified important characteristics of the varieties that were widely adopted in Central Luzon using farm surveys conducted in 1966–2012. We found that farmers adopt MVs that are high yielding, mature faster, and have long and slender grains, high milling recovery, and intermediate amylose content. The amylose content of adopted varieties has been declining, suggesting value in developing softer rice. To have a high potential for adoption, new MVs should have characteristics within the ranges of values observed for the adopted MVs. In addition, new MVs should have higher head rice recovery, less chalky grains, and better resistance to pests and diseases. Most MVs released in 2005–2013 compared poorly in these three traits. To reduce the risk of severe outbreaks, broad spectrum resistance should be incorporated into new MVs. This analysis of five decades of farm surveys provides insights into the varietal characteristics preferred by farmers which could contribute to the establishment of a product profile for developing improved MVs that are more targeted and, hence, would have high potential for adoption by farmers in Central Luzon and similar areas. We recommend a similar analysis be done in other major rice growing regions to aid the development of MVs that are more responsive to farmers’ needs and preferences.

Asian countries strongly depend on rice production for food security. The major rice-growing season (June to October) is highly exposed to the risk of tropical storm related damage. Unbiased and transparent approaches to assess the risk of rice crop damage are essential to support mitigation and disaster response strategies in the region. This study describes and demonstrates a method for rapid, pre-event crop status assessment. The ex-post test case is Typhoon Haiyan and its impact on the rice crop in Leyte Province in the Philippines. A synthetic aperture radar (SAR) derived rice area map was used to delineate the area at risk while crop status at the moment of typhoon landfall was estimated from specific time series analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) data. A spatially explicit indicator of risk of standing crop loss was calculated as the time between estimated heading date and typhoon occurrence. Results of the analysis of pre- and post-event SAR images showed that 6500 ha were flooded in northeastern Leyte. This area was also the region most at risk to storm related crop damage due to late establishment of rice. Estimates highlight that about 700 ha of rice (71% of which was in northeastern Leyte) had not reached maturity at the time of the typhoon event and a further 8400 ha (84% of which was in northeastern Leyte) were likely to be not yet harvested. We demonstrated that the proposed approach can provide pre-event, in-season information on the status of rice and other field crops and the risk of damage posed by tropical storms.

The objective of this study is to provide complete information on the dynamic relationship between X-band (3.11 cm) backscattering intensity (σ°) and rice crop’s leaf area index (LAI) at all growth phases. Though the relationship between X-band σ° and LAI has been previously explored, details on the relationship at the reproductive phase remain unstudied. LAI at the reproductive phase is important particularly at the heading stage where LAI reaches its maximum as it is closely related to grain yield, and at flowering stage where the total leaf area affects the amount of photosynthates. Therefore, this study examined the relationship of increasing LAI (vegetative to reproductive phase) and decreasing LAI (ripening phase) with TerraSAR-X (TSX) ScanSAR (3.11 cm) σ° at HH polarisation and 45° incidence angle. The results showed a statistically significant (R² = 0.51, p value < 0.001) non-linear relationship of LAI with σ° at the vegetative to reproductive phase while no significant linear relationship was found at the ripening phase. This study completes the response curve of X-band σ° to LAI by filling in the information on the reproductive phase which more accurately characterises the dynamic relationship between the rice crop’s LAI and X-band’s σ°. This contributes to improved knowledge on the use of X-band data for estimating LAI for the whole crop cycle which is essential for the modelling of crop growth and estimation of yield.

Occurrence of pests and diseases are influenced by several factors including weather, landscape and field-level factors such as crop management practices including crop establishment method. In this paper, we adopted and applied a method using Sentinel-1A (S-1A) Synthetic Aperture Radar (SAR) intensity to discriminate between rice fields that are transplanted and direct seeded to come up with a robust method for automated classification of crop establishment method. Multi-temporal S-1A C-band dual polarization images at 20m resolution covering the wet cropping season over four provinces in the Philippines were acquired from March to November 2018. Field measurements, observations and interviews were conducted on 186 sample fields and mean backscatter values for each of the sampled fields were generated from S-1A data acquired during the season. The reported dates of land preparation and estimated dates of crop growth stages were matched with the corresponding SAR acquisition dates. We used the Mann-Whitney U test to identify growth stages for which there are significant differences in backscatter values between transplanted and direct seeded rice. The results are generally consistent with the findings of a previous study conducted in one province in the Philippines in the dry season of 2017. We found, however, some inconsistencies in terms of the polarization where the significant differences were observed. These findings demonstrate the possibility of discriminating transplanted from direct seeded rice using SAR temporal data but suggests further fine tuning in the methodology is needed for different locations and seasons.