Elucidating the impact of temperature variability and extremes on cereal croplands through remote sensing

Modelling future climate effects on N2O emission and soil carbon storage in maize fields under controlled-release urea and straw incorporation

Controlled-release urea application and straw incorporation have been conducted in recent years as environmental-friendly and sustainable farming strategies, but the long-term effects of controlled-release urea application and combination with straw on the dryland maize yield, soil fertility and the environment under future climate scenarios remain unclear. Hence, based on a six-year field experiment, four treatments were used to calibrate and validate the DeNitrification-DeComposition (DNDC) model, including non-nitrogen (CK), split applications of conventional urea (UR), single basal application of conventional urea and controlled-release urea at a ratio of 2:1 (CU), and CU combined with straw (CUS). Subsequently, coupled the well-validated model with future climate to evaluate suitable agricultural production practices under two shared socioeconomic pathways (SSP)-SSP245 and SSP585. The validation results indicated a good fit between the simulated and observed data of greenhouse gas emissions, soil organic carbon (SOC) contents and maize yields. With the anticipation of warmer temperatures and increased precipitation in the future, the yields of UR, CU, and CUS treatment significantly rose. Under SSP585 scenario, the positive impacts of CU treatment on maize yields reduced after the 2050s, exhibiting an average decline of 12.03%. Compared with the UR treatment, the CU treatment markedly reduced cumulative N2O emissions, and both treatments maintained the original state of SOC storages in the 2030s, furthermore, the CUS treatment reduced N2O emissions by 47.10%, 35.07%, 23.80% and 10.04% in the 2030s, 2050s, 2070s and 2090s, respectively. SOC storages for the CUS treatment gradually increased with an average of 464.58, 350.22, 250.87 and 177.75 kg C ha-1 y-1 for two SSP scenarios in the 2030s, 2050s, 2070s and 2090s, which excellently offset the CO2 equivalent of emissions caused by N2O emissions. Therefore, in dryland maize production, combined controlled-release urea with straw incorporation could achieve the best comprehensive effect among increase of yield, improvement of SOC storages and alleviation of greenhouse gas emissions under future climate scenario.

An integrative data-driven approach for monitoring corn biomass under irrigation water and nitrogen levels based on UAV-based imagery

Unmanned aerial vehicle (UAV)-based remote sensing has been widely considered recently in field scale crop yield estimation. In this research, the capability of 13 spectral indices in the form of 5 groups was studied under different irrigation water and N fertilizer managements in terms of corn biomass monitoring and estimation. Farm experiments were conducted at Urmia University, Iran. The research was done using a randomized complete block design at three levels of 60, 80, and 100% of irrigation water and nitrogen requirements during four replications. The aerial imagery operations were performed using a fixed-wing UAV equipped with a Sequoia sensor during three plant growth stages including stem elongation, flowering, and silking. The effect of different irrigation water and nitrogen levels on vegetation indices and crop biomass was examined using variance decomposition analysis. Then, the correlation of the vegetation indices with corn biomass was evaluated by fitting linear regression models. Based on the obtained results, the indices based on near infrared (NIR) and red-edge spectral bands showed a better performance. Thus, the MERIS terrestrial chlorophyll index (MTCI) indicated the highest accuracy at estimating corn biomass during the growing season with the R2 and RMSE values of 0.92 and 8.27 ton/ha, respectively. Finally, some Bayesian model averaging (BMA) models were proposed to estimate corn biomass based on the selected indices and different spectral bands. Results of the BMA models revealed that the accuracy of biomass estimation models could be improved using the capabilities and advantages of different vegetation indices.

Evaluating area-specific adaptation strategies for rainfed maize under future climates of India

This study investigates the spatio-temporal changes in maize yield under projected climate and identified the potential adaptation measures to reduce the negative impact. Future climate data derived from 30 general circulation models were used to assess the impact of future climate on yield in 16 major maize growing districts of India. DSSAT model was used to simulate maize yield and evaluate adaptation strategies during mid (2040-69) and end-centuries (2070-99) under RCP 4.5 and 8.5. Genetic coefficients were calibrated and validated for each of the study locations. The projected climate indicated a substantial increase in mean seasonal maximum (0.9-6.0 °C) and minimum temperatures (1.1-6.1 °C) in the future (the range denotes the lowest and highest change during all the four future scenarios). Without adaptation strategies, climate change could reduce maize yield in the range of 16% (Tumkur) to 46% (Jalandhar) under RCP 4.5 and 21% (Tumkur) to 80% (Jalandhar) under RCP 8.5. Only at Dharwad, the yield could remain slightly higher or the same compared to the baseline period (1980-2009). Six adaptation strategies were evaluated (delayed sowing, increase in fertilizer dose, supplemental irrigation, and their combinations) in which a combination of those was found to be effective in majority of the districts. District-specific adaptation strategies were identified for each of the future scenarios. The findings of this study will enable in planning adaptation strategies to minimize the negative impact of projected climate in major maize growing districts of India.

Decoding the Karakoram Anomaly

The 'Karakoram Anomaly' is termed as the stability or anomalous growth of glaciers in the central Karakoram, in contrast to the retreat of glaciers in other nearby mountainous ranges of Himalayas and other mountainous ranges of the world. It remains an intriguing scientific question to the researchers. An attempt is made to provide mechanisms leading to such a process and thus 'affirming' it. In view of this, meteorological and cryospheric processes, viz., glacial-atmosphere coupled interactions in tandem with temperature-moisture interactions and radiative balance- on glaciated regions are simultaneously argued over the Karakoram and the adjacent Ladakh. Ladakh is deliberately chosen to compare the weaknesses, lacuna and gaps in the observations/reanalyzes- so that similar forcings are investigated over both regions. It is important to mention that both regions are data sparse. Findings show that geographical and elevation positioning of the Karakoram makes its environmental conditions conducive for glacier stability and/or growth which otherwise is not the case in the Ladakh region. Indian winter monsoon, western disturbances (WDs) embedded within upper level subtropical westerly jet moving eastwards, provides higher moisture incursion which in association with lowered lifting condensation level dumps higher moisture/mass over Karakoram than Ladakh. In addition, role of 2 m surface (T2m) and skin temperature (Ts) is one of the leading driving mechanisms. Difference (T2m-Ts) illustrates inversion which provides stable atmosphere leading to dump all the available moisture/mass over Karakoram, which is contrary over Ladakh.

A Method for Monitoring and Forecasting the Heading and Flowering Dates of Winter Wheat Combining Satellite-Derived Green-up Dates and Accumulated Temperature

Heading and flowering are two key phenological stages in the growth process of winter wheat. It is of great significance for agricultural management and scientific research to accurately monitor and forecast the heading and flowering dates of winter wheat. However, the monitoring accuracy of existing methods based on remote sensing needs to be improved, and these methods cannot realize forecasting in advance. This study proposed an accumulated temperature method (ATM) for monitoring and forecasting the heading and flowering dates of winter wheat from the perspective of thermal requirements for crop growth. The ATM method consists of three key procedures: (1) extracting the green-up date of winter wheat as the starting point of temperature accumulation with the dynamic threshold method from remotely sensed vegetation index (VI) time-series data, (2) calculating the accumulated temperature and determining the thermal requirements from the green-up date to the heading date or the flowering date based on phenology observation samples, and (3) combining the satellite-derived green-up date, daily temperature data, and thermal requirements to monitor and forecast the heading date and flowering date of winter wheat. When applying the ATM method to winter wheat in the North China Plain during 2017–2019, the root mean square error (RMSE) for the estimated heading date was between 4.76 and 6.13 d and the RMSE for the estimated flowering date was between 5.30 and 6.41 d. By contrast, the RMSE for the heading and flowering dates estimated by the widely used maximum vegetation index method was approximately 10 d. Furthermore, the forecasting accuracy of the ATM method was also high, and the RMSE was approximately 6 d. In summary, the proposed ATM method can be used to accurately monitor and forecast the heading and flowering dates of winter wheat in large spatial scales and it performs better than the existing maximum vegetation index method.

Detecting Frost Stress in Wheat: A Controlled Environment Hyperspectral Study on Wheat Plant Components and Implications for Multispectral Field Sensing

Radiant frost during the reproductive stage of plant growth can result in considerable wheat (Triticum aestivum L.) yield loss. Much effort has been spent to prevent and manage these losses, including post-frost remote sensing of damage. This study was done under controlled conditions to examine the effect of imposed frost stress on the spectral response of wheat plant components (heads and flag leaves). The approach used hyperspectral profiling to determine whether changes in wheat components were evident immediately after a frost (up to 5 days after frosting (DAF)). Significant differences were found between frost treatments, irrespective of DAF, in the Blue/Green (419–512 nanometers (nm)), Red (610–675 nm) and Near Infrared (NIR; 749–889 nm) regions of the electromagnetic spectrum (EMS) in head spectra, and in the Blue (415–494 nm), Red (670–687 nm) and NIR (727–889 nm) regions in the leaf spectra. Significant differences were found for an interaction between time and frost treatment in the Green (544–575 nm) and NIR (756–889 nm) in head spectra, and in the UV (394–396 nm) and Green/Red (564–641 nm) in leaf spectra. These findings were compared with spectral and temporal resolutions of commonly used field agricultural multispectral sensors to examine their potential suitability for frost damage studies at the canopy scale, based on the correspondence of their multispectral bands to the results from this laboratory-based hyperspectral study.

Text Mining in Remotely Sensed Phenology Studies: A Review on Research Development, Main Topics, and Emerging Issues

As an interdisciplinary field of research, phenology is developing rapidly, and the contents of phenological research have become increasingly abundant. In addition, the potentiality of remote sensing technologies has largely contributed to the growth and complexity of this discipline, in terms of the scale of analysis, techniques of data processing, and a variety of topics. As a consequence, it is increasingly difficult for scientists to get a clear picture of remotely sensed phenology (rs+pheno) research. Bibliometric analysis is increasingly used for the study of a discipline and its conceptual dynamics. This review analyzed the last 40 years (1979–2018) of publications in the rs+pheno field retrieved from the Scopus database; such publications were investigated by means of a text mining approach, both in terms of bibliographic and text data. Results demonstrated that rs+pheno research is exponentially growing through time; however, it is primarily considered a subset of remote sensing science rather than a branch of phenology. In this framework, in the last decade, agriculture is becoming more and more a standalone science in rs+pheno research, independently from other related topics, e.g., classification. On the contrary, forestry struggles to gain its thematic role in rs+pheno studies and remains strictly connected with climate change issues. Classification and mapping represent the major rs+pheno topic, together with the extraction and the analysis of phenological metrics, like the start of the growing season. To the contrary, forest ecophysiology, in terms of ecosystem respiration and net ecosystem exchange, results as the most relevant new topic, together with the use of the red edge band and SAR (Synthetic Aperture Radar) data in rs+pheno agricultural studies. Some niche emerging rs+pheno topics may be recognized in the ocean and arctic investigations linked to phytoplankton blooming and ice cover dynamics. The findings of this study might be applicable for planning and managing remotely sensed phenology research; scientists involved in such discipline might use this study as a reference to consider their research domain in a broader dynamical network.

Satellite sun-induced chlorophyll fluorescence detects early response of winter wheat to heat stress in the Indian Indo-Gangetic Plains

Extremely high temperatures represent one of the most severe abiotic stresses limiting crop productivity. However, understanding crop responses to heat stress is still limited considering the increases in both the frequency and severity of heat wave events under climate change. This limited understanding is partly due to the lack of studies or tools for the timely and accurate monitoring of crop responses to extreme heat over broad spatial scales. In this work, we use novel spaceborne data of sun-induced chlorophyll fluorescence (SIF), which is a new proxy for photosynthetic activity, along with traditional vegetation indices (Normalized Difference Vegetation Index NDVI and Enhanced Vegetation Index EVI) to investigate the impacts of heat stress on winter wheat in northwestern India, one of the world's major wheat production areas. In 2010, an abrupt rise in temperature that began in March adversely affected the productivity of wheat and caused yield losses of 6% compared to previous year. The yield predicted by satellite observations of SIF decreased by approximately 13.9%, compared to the 1.2% and 0.4% changes in NDVI and EVI, respectively. During early stage of this heat wave event in early March 2010, the SIF observations showed a significant reduction and earlier response, while NDVI and EVI showed no changes and could not capture the heat stress until late March. The spatial patterns of SIF anomalies closely tracked the temporal evolution of the heat stress over the study area. Furthermore, our results show that SIF can provide large-scale, physiology-related wheat stress response as indicated by the larger reduction in fluorescence yield (SIF_yield ) than fraction of photosynthetically active radiation during the grain-filling phase, which may have eventually led to the reduction in wheat yield in 2010. This study implies that satellite observations of SIF have great potential to detect heat stress conditions in wheat in a timely manner and assess their impacts on wheat yields at large scales.

Feature selection approaches for predictive modelling of groundwater nitrate pollution: An evaluation of filters, embedded and wrapper methods

Recognising the various sources of nitrate pollution and understanding system dynamics are fundamental to tackle groundwater quality problems. A comprehensive GIS database of twenty parameters regarding hydrogeological and hydrological features and driving forces were used as inputs for predictive models of nitrate pollution. Additionally, key variables extracted from remotely sensed Normalised Difference Vegetation Index time-series (NDVI) were included in database to provide indications of agroecosystem dynamics. Many approaches can be used to evaluate feature importance related to groundwater pollution caused by nitrates. Filters, wrappers and embedded methods are used to rank feature importance according to the probability of occurrence of nitrates above a threshold value in groundwater. Machine learning algorithms (MLA) such as Classification and Regression Trees (CART), Random Forest (RF) and Support Vector Machines (SVM) are used as wrappers considering four different sequential search approaches: the sequential backward selection (SBS), the sequential forward selection (SFS), the sequential forward floating selection (SFFS) and sequential backward floating selection (SBFS). Feature importance obtained from RF and CART was used as an embedded approach. RF with SFFS had the best performance (mmce=0.12 and AUC=0.92) and good interpretability, where three features related to groundwater polluted areas were selected: i) industries and facilities rating according to their production capacity and total nitrogen emissions to water within a 3km buffer, ii) livestock farms rating by manure production within a 5km buffer and, iii) cumulated NDVI for the post-maximum month, being used as a proxy of vegetation productivity and crop yield.

Ensemble yield simulations: Using heat-tolerant and later-maturing varieties to adapt to climate warming

The use of modern crop varieties is a dominant method of obtaining high yields in crop production. Efforts to identify suitable varieties, with characteristics that would increase crop yield under future climate conditions, remain essential to developing sustainable agriculture and food security. This work aims to evaluate potential genotypic adaptations (i.e., using varieties with increased ability to produce desirable grain numbers under high temperatures and with enhanced thermal time requirements during the grain-filling period) to cope with the negative impacts of climate change on maize yield. The contributions of different options were investigated at six sites in the North China Plain using the APSIM model and the outputs of 8 GCMs under RCP4.5 scenarios. It was found that without considering adaptation options, mean maize yield would decrease by 7~18% during 2010–2039 relative to 1976–2005. A large decrease in grain number relative to stabilized grain weight decreased maize yield under future climate scenarios. Using heat-tolerant varieties, maize yield could increase on average by 6% to 10%. Using later maturing varieties, e.g., enhanced thermal time requirements during the grain-filling period, maize yield could increase by 7% to 10%. The optimal adaptation options were site specific.

Functional cereals for production in new and variable climates

Adaptation of cereal crops to variable or changing climates requires that essential quality attributes are maintained to deliver food that will be acceptable to human consumers. Advances in cereal genomics are delivering insights into the molecular basis of nutritional and functional quality traits in cereals and defining new genetic resources. Understanding the influence of the environment on expression of these traits will support the retention of these essential functional properties during climate adaptation. New cereals for use as whole grain or ground to flour for other food products may be based upon the traditional species such as rice and wheat currently used in these food applications but may also include new options exploiting genomics tools to allow accelerated domestication of new species.

Rubisco catalytic properties of wild and domesticated relatives provide scope for improving wheat photosynthesis

Rubisco is a major target for improving crop photosynthesis and yield, yet natural diversity in catalytic properties of this enzyme is poorly understood. Rubisco from 25 genotypes of the Triticeae tribe, including wild relatives of bread wheat (Triticum aestivum), were surveyed to identify superior enzymes for improving photosynthesis in this crop. In vitro Rubisco carboxylation velocity (V c), Michaelis-Menten constants for CO2 (K c) and O2 (K o) and specificity factor (S c/o) were measured at 25 and 35 °C. V c and K c correlated positively, while V c and S c/o were inversely related. Rubisco large subunit genes (rbcL) were sequenced, and predicted corresponding amino acid differences analysed in relation to the corresponding catalytic properties. The effect of replacing native wheat Rubisco with counterparts from closely related species was analysed by modelling the response of photosynthesis to varying CO2 concentrations. The model predicted that two Rubisco enzymes would increase photosynthetic performance at 25 °C while only one of these also increased photosynthesis at 35 °C. Thus, under otherwise identical conditions, catalytic variation in the Rubiscos analysed is predicted to improve photosynthetic rates at physiological CO2 concentrations. Naturally occurring Rubiscos with superior properties amongst the Triticeae tribe can be exploited to improve wheat photosynthesis and crop productivity.