Classification of Daily Crop Phenology in PhenoCams Using Deep Learning and Hidden Markov Models

Near-surface cameras, such as those in the PhenoCam network, are a common source of ground truth data in modelling and remote sensing studies. Despite having locations across numerous agricultural sites, few studies have used near-surface cameras to track the unique phenology of croplands. Due to management activities, crops do not have a natural vegetation cycle which many phenological extraction methods are based on. For example, a field may experience abrupt changes due to harvesting and tillage throughout the year. A single camera can also record several different plants due to crop rotations, fallow fields, and cover crops. Current methods to estimate phenology metrics from image time series compress all image information into a relative greenness metric, which discards a large amount of contextual information. This can include the type of crop present, whether snow or water is present on the field, the crop phenology, or whether a field lacking green plants consists of bare soil, fully senesced plants, or plant residue. Here, we developed a modelling workflow to create a daily time series of crop type and phenology, while also accounting for other factors such as obstructed images and snow covered fields. We used a mainstream deep learning image classification model, VGG16. Deep learning classification models do not have a temporal component, so to account for temporal correlation among images, our workflow incorporates a hidden Markov model in the post-processing. The initial image classification model had out of sample F1 scores of 0.83–0.85, which improved to 0.86–0.91 after all post-processing steps. The resulting time series show the progression of crops from emergence to harvest, and can serve as a daily, local-scale dataset of field states and phenological stages for agricultural research.

Improved representation of agricultural land use and crop management for large-scale hydrological impact simulation in Africa using SWAT+

To date, most regional and global hydrological models either ignore the representation of cropland or consider crop cultivation in a simplistic way or in abstract terms without any management practices. Yet, the water balance of cultivated areas is strongly influenced by applied management practices (e.g. planting, irrigation, fertilization, and harvesting). The SWAT+ (Soil and Water Assessment Tool) model represents agricultural land by default in a generic way, where the start of the cropping season is driven by accumulated heat units. However, this approach does not work for tropical and subtropical regions such as sub-Saharan Africa, where crop growth dynamics are mainly controlled by rainfall rather than temperature. In this study, we present an approach on how to incorporate crop phenology using decision tables and global datasets of rainfed and irrigated croplands with the associated cropping calendar and fertilizer applications in a regional SWAT+ model for northeastern Africa. We evaluate the influence of the crop phenology representation on simulations of leaf area index (LAI) and evapotranspiration (ET) using LAI remote sensing data from Copernicus Global Land Service (CGLS) and WaPOR (Water Productivity through Open access of Remotely sensed derived data) ET data, respectively. Results show that a representation of crop phenology using global datasets leads to improved temporal patterns of LAI and ET simulations, especially for regions with a single cropping cycle. However, for regions with multiple cropping seasons, global phenology datasets need to be complemented with local data or remote sensing data to capture additional cropping seasons. In addition, the improvement of the cropping season also helps to improve soil erosion estimates, as the timing of crop cover controls erosion rates in the model. With more realistic growing seasons, soil erosion is largely reduced for most agricultural hydrologic response units (HRUs), which can be considered as a move towards substantial improvements over previous estimates. We conclude that regional and global hydrological models can benefit from improved representations of crop phenology and the associated management practices. Future work regarding the incorporation of multiple cropping seasons in global phenology data is needed to better represent cropping cycles in areas where they occur using regional to global hydrological models.

Trends and Climate Response in the Phenology of Crops in Northeast China

Crop phenology is the process of crop growth and yield formation, which is largely driven by climatic conditions. It is vital to investigate the shifts in crop phenological processes in response to climate variability. Previous studies often only explored the response of a single crop phenology to climate change, and lacked comparative studies on the climate response in different crop phenology. We intend to investigate the trends in phenological change of three typical crops (i.e., maize, rice and soybean) in Northeast China (NEC) and their response to climate change during 1981–2010. Its main purpose is to reveal the differences in the sensitivity of different crop phenology to key climate factors [e.g., mean temperature (T), accumulated precipitation (AP) and accumulated sunshine hours (AS) during the crop growth period]. We found that the three crops have different phenological changes and varying ranges, and significant spatial heterogeneity in phenological changes. The results indicated that the lengths of different crop growth stages [e.g., the vegetative growth period (VGP), the reproductive growth period (RGP) and the whole growth period (WGP)] were negatively correlated with T, especially in VGP and WGP. However, the lengths of growth period of the three crops were positively correlated with AP and AS. For each 1°C increase in T, the number of days shortened in WGP (about 5 days) was the largest, and that in RGP (less than 2 days) was the smallest. Therefore, the increases in T during past 3 decades have significantly shortened VGP and WGP of three crops, but had slight and inconsistent effects on RGP. Moreover, changes in AP has slight impact on the growth periods of maize and rice, and significantly shortened RGP and WGP of soybean. Changes in AS exerted important and inconsistent effects on the phenology of three crops. This study indicated that there are significant differences in the sensitivity and response of different crop phenology to climate factors. Therefore, in evaluating the response and adaptation of crops to climate change, comparison and comprehensive analysis of multiple crops are helpful to deeply understand the impact of climate change on crop production.

Agrometeorological Indices, Crop Phenology and Yield of Pigeon pea as Influenced by Pigeon pea (Cajanus cajan L.) based Intercropping System

A field investigation was conducted at experimental farm, Department of Agricultural Meteorology, located at college of Agriculture, V.N.M.K.V, Parbhani during kharif season of 2019-20. The experiment was laid out in RBD with three replication, under this study there were nine treatments viz. T1 (Pigeon pea + Sorghum), T2 (Pigeon pea + Maize), T3 (Pigeon pea + Soybean), T4 (Pigeon pea + Sesamum), T5 (Pigeon pea), T6 (Sorghum), T7 (Maize), T8 (Soybean), T9 (Sesamum). In pigeon pea the highest total agrometeorological indices (GDD, HTU and PTU) accumulated by intercropped treatment T1 as compared to sole, by sorghum, maize and sesamum was highest in intercropped treatment i.e. (T1), (T2) and (T4) than in sole whereas, the accumulated agrometeorological indices by soybean was highest in sole treatment i.e. (T8) than intercropped (T3). Significantly higher Pigeon pea equivalent yield was attained with treatment T3 followed by T4, lowest recorded in T1 intercropping system. The highest stalk / stover yield was attained by T2 as compared to sole whereas, lowest was recorded in T8. Treatment T3 performed better than other and this treatment was better in terms of growth and yield attributing characters.

Impact of global warming (1.5ºC) on the productivity of selected C3 and C4 crops across Tamil Nadu

Over the last century, mean annual temperatures increased by ~1°C. UNFCCC has proposed to limit warming below 1.5°C relative to pre-industrial levels. A study was conducted on rice (C3 pathway) and maize (C4 pathway) over Tamil Nadu using DSSAT to understand the climate change impacts with projected temperature increase of 1.5°C.The future climate under RCP 4.5 and RCP 8.5 indicated 1.5°Cincrease in temperature to happen by 2053 and 2035, respectively over Tamil Nadu.Annual rainfall deviations in RCP4.5 showed drier than current condition and RCP8.5 projected wetter SWM and drier NEM (90 % of current rainfall).Impact of 1.5°C warming on crop phenology indicated 8 days reduction in duration for rice and maize. The W UE of rice would decrease by 17 per cent at current CO2 whereas, enrichment (430 ppm) would reduce by12 per cent and rice yield is reduced by 21 per cent with 360 ppm CO2 and 430 ppm reducedby 17 per cent. There is no considerable varaition (- 5 to 1 %) in maize productivity with 1.5 ºC warming. The above results indicated that 1.5 ºC warming has more negative impacts on plants with C3 compared to C4 pathway