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The objective of this work was to test long-term trends in the planting date of gladiolus to ensure marketing of these flowers on Mother’s Day and All Souls’ Day in Santa Maria (latitude: 29° 43’ S, longitude: 53° 43’ W, and altitude: 95 m), Rio Grande do Sul State, Brazil. Minimum and maximum air temperature data from 106 years were used (1912-2017) to simulate the optimum planting date indicated through the PhenoGlad model, aiming to harvest floral stems for both market dates for early, intermediate I, intermediate II and late cultivars. The homogeneity of the historical series was tested using the run test, and the historical trend was tested by the Mann-Kendal test. The magnitude of the trend was estimated with simple linear regression, and the descriptive statistics were calculated. For marketing on Mother’s Day, there was no historical trend that implied a change in the planting date of gladiolus for any of the development cycles. For marketing on All Souls’ Day, there was a positive historical trend only for the early and intermediate cycles I and II; thus, the increase in air temperature implied a delay of 9.2 days, 9.5 days and 6.9 days for the planting date, respectively, indicating that a shortening of the gladiolus development cycle occurred, mainly in late winter/early spring.

Abstract The lack of spatially detailed crop calendars is a significant source of uncertainty in modeling, monitoring, and forecasting crop production. In this paper, we present a rule‐based model to estimate the sowing and harvesting windows of major crops over the global land area. The model considers field workability due to snow cover and heavy rainfall in addition to crop biological requirements for heat, chilling, and moisture. Using daily weather data for the period 1996–2005 as model input, we derive calendars for maize, rice, winter and spring wheat, and soybeans around the year 2000 with a spatial resolution of 0.5° in latitude and longitude. Separate calendars for rainfed and irrigated conditions and three representative varieties (short‐, medium‐ and long‐season varieties) are estimated. The daily probabilities of sowing and harvesting derived using the model well capture the major characteristics of reported calendars. Our modeling reveals that field workability is an important determinant of sowing and harvesting dates and that multicropping patterns influence the calendars of individual crops. The case studies show that the model is capable of capturing multicropping patterns such as triple rice cropping in Bangladesh, double rice cropping in the Philippines, winter wheat‐maize rotations in France, and maize‐winter wheat‐soybean rotations in Brazil. The model outputs are particularly valuable for agricultural and hydrological applications in regions where existing crop calendars are sparse or unreliable.

Different rice crop information can be derived from different remote sensing sources to provide information for decision making and policies related to agricultural production and food security. The objective of this study is to generate complementary and comprehensive rice crop information from hypertemporal optical and multitemporal high-resolution SAR imagery. We demonstrate the use of MODIS data for rice-based system characterization and X-band SAR data from TerraSAR-X and CosmoSkyMed for the identification and detailed mapping of rice areas and flooding/transplanting dates. MODIS was classified using ISODATA to generate cropping calendar, cropping intensity, cropping pattern and rice ecosystem information. Season and location specific thresholds from field observations were used to generate detailed maps of rice areas and flooding/transplanting dates from the SAR data. Error matrices were used for the accuracy assessment of the MODIS-derived rice characteristics map and the SAR-derived detailed rice area map, while Root Mean Square Error (RMSE) and linear correlation were used to assess the TSX-derived flooding/transplanting dates. Results showed that multitemporal high spatial resolution SAR data is effective for mapping rice areas and flooding/transplanting dates with an overall accuracy of 90% and a kappa of 0.72 and that hypertemporal moderate-resolution optical imagery is effective for the basic characterization of rice areas with an overall accuracy that ranged from 62% to 87% and a kappa of 0.52 to 0.72. This study has also provided the first assessment of the temporal variation in the backscatter of rice from CSK and TSX using large incidence angles covering all rice crop stages from pre-season until harvest. This complementarity in optical and SAR data can be further exploited in the near future with the increased availability of space-borne optical and SAR sensors. This new information can help improve the identification of rice areas.