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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.

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 FROM AUTHOR]

Abstract Global and regional scale agricultural monitoring systems aim to provide up-to-date information regarding food production to different actors and decision makers in support of global and national food security. To help reduce price volatility of the kind experienced between 2007 and 2011, a global system of agricultural monitoring systems is needed to ensure the coordinated flow of information in a timely manner for early warning purposes. A number of systems now exist that fill this role. This paper provides an overview of the eight main global and regional scale agricultural monitoring systems currently in operation and compares them based on the input data and models used, the outputs produced and other characteristics such as the role of the analyst, their interaction with other systems and the geographical scale at which they operate. Despite improvements in access to high resolution satellite imagery over the last decade and the use of numerous remote-sensing based products by the different systems, there are still fundamental gaps. Based on a questionnaire, discussions with the system experts and the literature, we present the main gaps in the data and in the methods. Finally, we propose some recommendations for addressing these gaps through ongoing improvements in remote sensing, harnessing new and innovative data streams and the continued sharing of more and more data. Highlights • Eight global and regional agricultural monitoring systems are compared • Gaps in data are described, where cropland maps, crop calendars and meteorological data are viewed as the most critical • Gaps in methods include the need for better predictions of yield and crop production and how to operationalize research methods • New sources of remote sensing data, greater data sharing and new sources of information, e.g. from mobile phones, will all help to improve agricultural monitoring [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: This manuscript describes a numerical model to estimate location‐specific sowing and harvesting dates of crops over the globe. Ten‐year‐long daily weather data and a few coefficients that represent the physiological characteristics of a crop (for instance, the amount of water needs to complete the life cycle of an annual crop) are only inputs to the model. Comparisons with the reported crop calendars indicate that the model well reproduces calendars of maize, rice, winter and spring wheat, and soybean around the year 2000 in major crop‐producing countries. We also find that snow cover and heavy rainfall, which influence field workability but have not considered in earlier modeling, are important to estimate sowing and harvesting dates and multicropping patterns (for instance, a combination of winter and summer crops) affect the calendar of individual crops. Our findings are useful when simulating the responses of crop calendars to climate change. Key Points: The model estimates the daily probabilities of sowing and harvesting in the year 2000Winter and spring wheat and rainfed versus irrigated conditions are differentiatedOur findings have implications to improve modeling multicropping patterns [ABSTRACT FROM AUTHOR]

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.

Aim To derive location-specific parameters that reflect the geographic differences among cultivars in vernalization requirements, sensitivity to day length (photoperiod) and temperature, which can be used to simulate the phenological development of wheat and maize at the global scale. Location Global. Methods Based on crop calendar observations and literature describing the large-scale patterns of phenological characteristics of cultivars, we developed algorithms to compute location-specific parameters to represent this large-scale pattern. Vernalization requirements were related to the duration and coldness of winter, sensitivity to day length was assumed to be represented by the minimum and maximum day lengths occurring at a location, and sensitivity to temperature was related to temperature conditions during the vegetative development phase of the crop. Results Application of the derived location-specific parameters resulted in high agreement between simulated and observed lengths of the cropping period. Agreement was especially high for wheat, with mean absolute errors of less than 3 weeks. In the main maize cropping regions, cropping periods were over- and underestimated by 0.5-1.5 months. We also found that interannual variability in simulated wheat harvest dates was more realistic when accounting for photoperiod effects. Main conclusions The methodology presented here provides a good basis for modelling the phenological characteristics of cultivars at the global scale. We show that current global patterns of growing season length as described in cropping calendars can be largely reproduced by phenology models if location-specific parameters are derived from temperature and day length indicators. Growing seasons can be modelled more accurately for wheat than for maize, especially in warm regions. Our method for computing parameters for phenology models from temperature and day length offers opportunities to improve the simulation of crop productivity by crop simulation models developed for large spatial areas and for long-term climate impact projections that account for adaptation in the selection of varieties. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aim To assemble a data set of global crop planting and harvesting dates for 19 major crops, explore spatial relationships between planting date and climate for two of them, and compare our analysis with a review of the literature on factors that drive decisions on planting dates. Location Global. Methods We digitized and georeferenced existing data on crop planting and harvesting dates from six sources. We then examined relationships between planting dates and temperature, precipitation and potential evapotranspiration using 30-year average climatologies from the Climatic Research Unit, University of East Anglia (CRU CL 2.0). Results We present global planting date patterns for maize, spring wheat and winter wheat (our full, publicly available data set contains planting and harvesting dates for 19 major crops). Maize planting in the northern mid-latitudes generally occurs in April and May. Daily average air temperatures are usually c. 12–17 °C at the time of maize planting in these regions, although soil moisture often determines planting date more directly than does temperature. Maize planting dates vary more widely in tropical regions. Spring wheat is usually planted at cooler temperatures than maize, between c. 8 and 14 °C in temperate regions. Winter wheat is generally planted in September and October in the northern mid-latitudes. Main conclusions In temperate regions, spatial patterns of maize and spring wheat planting dates can be predicted reasonably well by assuming a fixed temperature at planting. However, planting dates in lower latitudes and planting dates of winter wheat are more difficult to predict from climate alone. In part this is because planting dates may be chosen to ensure a favourable climate during a critical growth stage, such as flowering, rather than to ensure an optimal climate early in the crop's growth. The lack of predictability is also due to the pervasive influence of technological and socio-economic factors on planting dates. [ABSTRACT FROM AUTHOR]