Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data.

The unprecedented availability of optical satellite data in cloud-based computing platforms, such as Google Earth Engine (GEE), opens new possibilities to develop crop trait retrieval models from the local to the planetary scale. Hybrid retrieval models are of interest to run in these platforms as they combine the advantages of physically- based radiative transfer models (RTM) with the flexibility of machine learning regression algorithms. Previous research with GEE primarily relied on processing bottom-of-atmosphere (BOA) reflectance data, which requires atmospheric correction. In the present study, we implemented hybrid models directly into GEE for processing Sentinel-2 (S2) Level-1C (L1C) top-of-atmosphere (TOA) reflectance data into crop traits. To achieve this, a training dataset was generated using the leaf-canopy RTM PROSAIL in combination with the atmospheric model 6SV. Gaussian process regression (GPR) retrieval models were then established for eight essential crop traits namely leaf chlorophyll content, leaf water content, leaf dry matter content, fractional vegetation cover, leaf area index (LAI), and upscaled leaf variables (i.e., canopy chlorophyll content, canopy water content and canopy dry matter content). An important pre-requisite for implementation into GEE is that the models are sufficiently light in order to facilitate efficient and fast processing. Successful reduction of the training dataset by 78% was achieved using the active learning technique Euclidean distance-based diversity (EBD). With the EBD-GPR models, highly accurate validation results of LAI and upscaled leaf variables were obtained against in situ field data from the validation study site Munich-North-Isar (MNI), with normalized root mean square errors (NRMSE) from 6% to 13%. Using an independent validation dataset of similar crop types (Italian Grosseto test site), the retrieval models showed moderate to good performances for canopy-level variables, with NRMSE ranging from 14% to 50%, but failed for the leaf-level estimates. Obtained maps over the MNI site were further compared against Sentinel-2 Level 2 Prototype Processor (SL2P) vegetation estimates generated from the ESA Sentinels' Application Platform (SNAP) Biophysical Processor, proving high consistency of both retrievals (R 2 from 0.80 to 0.94). Finally, thanks to the seamless GEE processing capability, the TOA-based mapping was applied over the entirety of Germany at 20 m spatial resolution including information about prediction uncertainty. The obtained maps provided confidence of the developed EBD-GPR retrieval models for integration in the GEE framework and national scale mapping from S2-L1C imagery. In summary, the proposed retrieval workflow demonstrates the possibility of routine processing of S2 TOA data into crop traits maps at any place on Earth as required for operational agricultural applications.

Large potential for crop production adaptation depends on available future varieties.

Climate change affects global agricultural production and threatens food security. Faster phenological development of crops due to climate warming is one of the main drivers for potential future yield reductions. To counter the effect of faster maturity, adapted varieties would require more heat units to regain the previous growing period length. In this study, we investigate the effects of variety adaptation on global caloric production under four different future climate change scenarios for maize, rice, soybean, and wheat. Thereby, we empirically identify areas that could require new varieties and areas where variety adaptation could be achieved by shifting existing varieties into new regions. The study uses an ensemble of seven global gridded crop models and five CMIP6 climate models. We found that 39% (SSP5-8.5) of global cropland could require new crop varieties to avoid yield loss from climate change by the end of the century. At low levels of warming (SSP1-2.6), 85% of currently cultivated land can draw from existing varieties to shift within an agro-ecological zone for adaptation. The assumptions on available varieties for adaptation have major impacts on the effectiveness of variety adaptation, which could more than half in SSP5-8.5. The results highlight that region-specific breeding efforts are required to allow for a successful adaptation to climate change.

Growth and grain yield of eight maize hybrids are aligned with water transport, stomatal conductance, and photosynthesis in a semi-arid irrigated system.

There is increasing interest in understanding how trait networks can be manipulated to improve the performance of crop species. Working towards this goal, we have identified key traits linking the acquisition of water, the transport of water to the sites of evaporation and photosynthesis, stomatal conductance, and growth across eight maize hybrid lines grown under well-watered and water-limiting conditions in Northern Colorado. Under well-watered conditions, hybrids with higher end-of-season growth and grain yield exhibited higher leaf-specific conductance, lower operating water potentials, higher rates of midday stomatal conductance, higher rates of net CO2 assimilation, and greater leaf osmotic adjustment. This trait network was similar under water-limited conditions with the notable exception that linkages between water transport, midday stomatal conductance, and growth were even stronger than under fully watered conditions. The maintenance of high leaf-specific conductance throughout the day was achieved via higher maximal conductance rates rather than lower susceptibility to conductance loss. Our results suggest that efforts to improve maize performance in well-watered and water-limiting conditions would benefit from considering the physiological trait networks governing water and carbon flux rather than focusing on single traits independently of one another.

Role of epigenetic modifications in the development of crops essential traits.

Epigenetic modification refers to the chemical modifications of chromosomal DNA and histones, mainly including DNA methylation, histone modifications and non-coding RNAs. Without altering the DNA sequence, these heritable modifications can affect gene expression profiles by changing the chromatin state and play an important role in regulating the growth and development of plants. When the specific epigenetic modifications are changed, crops can obtain excellent phenotypes and stronger environmental adaptability. Therefore, artificially changing the epigenetic modifications are expected to obtain high-quality germplasm resources more suitable for agricultural production. In this review, we summarize the main types of plant epigenetic modifications, highlight the research progresses of functional plant epigenetic modifications on the important traits and responses to environmental stress, and identify the main problems that need be solved in the application of epigenetics in crop improvement, thereby providing new insights for the functional epigenetic modifications on crop breeding and improvement.

The challenges of commercializing second-generation transgenic crop traits necessitate the development of international public sector research infrastructure.

It has been 30 years since the first transformation of a gene into a plant species, and since that time a number of biotechnology products have been developed, with the most important being insect- and herbicide-resistant crops. The development of second-generation products, including nutrient use efficiency and tolerance to important environmental stressors such as drought, has, up to this time, been less successful. This is in part due to the inherent complexities of these traits and in part due to limitations in research infrastructure necessary for public sector researchers to test their best ideas. Here we discuss lessons from previous work in the generation of the first-generation traits, as well as work from our labs and others on identifying genes for nitrogen use efficiency. We then describe some of the issues that have impeded rapid progress in this area. Finally, we propose the type of public sector organization that we feel is necessary to make advances in important second-generation traits such as nitrogen use efficiency.

Plant growth and stress-regulating metabolite response to biochar utilization boost crop traits and soil health.

Introduction: The utilization of biochar (BC) as a soil amendment in agriculture has gained significant traction among many farmers and researchers, primarily due to its eco-friendly role in boosting crop output. However, the performance of specific metabolites (e.g., zeatin, melatonin, sucrose, and phenyllactic acid) in the various tissues of sugarcane plant (leaf, stem, and root) and rhizosphere soil-deemed plant growth and stress regulators in a long-term BC-amended field remains poorly understood. Additionally, literature on the shift in soil attributes and crop growth triggered by the strong response of these bioactive compounds to longterm BC utilization remains undocumented. Methods: Metabolome integrated with highthroughput sequencing analyses were conducted to identify and quantify the performance of plant growth and stress-regulating metabolites in a long-term BC-amended field. Additionally, we investigated how the response of these compounds to BC-treated soil influences crop traits and soil biochemical properties. Results: We also identified and quantified the performance of pathogenic bacteria and unraveled the association between these compounds and potential plant growth-promoting bacteria. The BC-supplemented soil significantly boosted the crop traits, including brix, sucrose content, and chlorophyll, as well as soil nutrients, such as soil total nitrogen (TN), ammonium (NH4 +-N), and nitrate (NO3 --N). We also noticed that metabolite-deemed plant growth and stress regulators, including melatonin and phenyllactic acid, were enriched considerably in the stem and root tissues of the BC-amended soil. Zeatin in the leaf, stem, and root tissues exhibited the same trend, followed by sucrose in the leaf tissue of the BC-treated soil, implying that the strong response of these compounds to BC utilization contributed to the promotion of crop traits and soil quality. Pathogenic bacteria belonging to Proteobacteria and Acidobacteria were suppressed under the BC-supplemented soil, especially in the root tissue and rhizosphere soil, whereas plant growth-regulating bacteria, mainly Bradyrhizobium, responded strongly and positively to several metabolites. Discussion: Our finding provides valuable information for agronomists, farmers, and environmentalists to make informed decisions about crop production, land use, and soil management practices. Proper soil assessment and understanding of the interaction between the attributes of soil, BC, and metabolites are essential for promoting sustainable agriculture practices and land conservation.

Noncoding RNAs and their roles in regulating the agronomic traits of crops.

Molecular breeding is one of the most effective methods for improving the performance of crops. Understanding the genome features of crops, especially the physiological functions of individual genes, is of great importance to molecular breeding. Evidence has shown that genomes of both animals and plants transcribe numerous non-coding RNAs, which are involved in almost every aspect of development. In crops, an increasing number of studies have proven that non-coding RNAs are new genetic resources for regulating crop traits. In this review, we summarize the current knowledge of non-coding RNAs, which are potential crop trait regulators, and focus on the functions of long non-coding RNAs (lncRNAs) in determining crop grain yield, phased small-interfering RNAs (phasiRNAs) in regulating fertility, small interfering RNAs (siRNAs) and microRNAs (miRNAs) in facilitating plant immune response and disease resistance, and miRNAs mediating nutrient and metal stress. Finally, we also discuss the next-generation method for ncRNA application in crop domestication and breeding.

Crop improvement influences on water quantity and quality processes in an agricultural watershed.

Field crop traits have and are experiencing significant changes due to genetic and agronomic improvements. How these changes affect regional water quantity and quality processes has not been clarified. The St. Joseph River Watershed (SJRW) located in the U.S. Corn Belt was selected as a case study area. Crop (corn and soybean) trait improvements in the past decades were reviewed and summarized and include changes of growing degree days (GDD), leaf area index (LAI), light utilization (LU), drought tolerance (DT), nutrient content (NC), and harvest index (HI). Based on a calibrated 9-year (from 2011 to 2019) SWAT (Soil and Water Assessment Tool) simulation in SJRW, sensitivities of the above crop traits to yield, ETa, stream flow, tile flow, surface runoff, and nutrient loads (NO3N, TN, soluble-P, and TP) were analyzed. Crop traits and their corresponding SWAT parameters for the 2010s were obtained from model calibration and used as the baseline/current scenario; for the 1980s, they were summarized from literature review and used as an historical scenario, while those for the 2040s were determined by assuming crop traits are changing linearly with time and projected as the future scenario. Water quantity and quality changes under the historical and future crop scenarios were compared with the baseline/current simulation. Results showed LU and DT were the most sensitive crop traits to water quantity (i.e., ETa, stream flow, tile flow, and surface runoff), while HI was the most sensitive to nutrient loads. The impacts of crop improvements on nutrient loads were more significant than on water budgets. Compared with the baseline, the historical and future scenarios resulted in 1.5 - 2.0% changes of stream flow, 6.8 - 18.6% changes of nitrogen loads (NO3N and TN) and 2.6 - 3.9% changes of phosphorus loads (soluble-P, and TP) in the stream flow, annually. Moreover, in certain months, these changes can reach about 12% for stream flow, 42% for nitrogen loads, and 12% for phosphorus loads. Nitrogen losses by tile drainage and percolation, and phosphorus losses by surface runoff and tile drainage were most significantly affected by the crop improvements. Future work should consider expected crop improvements when studying long-term hydrology and nutrient cycles in agricultural watersheds.

Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression.

Monitoring cropland phenology from optical satellite data remains a challenging task due to the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to overcome these challenges and gain better knowledge of crop dynamics. The arrival of cloud computing platforms such as Google Earth Engine (GEE) has enabled us to propose a Sentinel-2 (S2) phenology end-to-end processing chain. To achieve this, the following pipeline was implemented: (1) the building of hybrid Gaussian Process Regression (GPR) retrieval models of crop traits optimized with active learning, (2) implementation of these models on GEE (3) generation of spatiotemporally continuous maps and time series of these crop traits with the use of gap-filling through GPR fitting, and finally, (4) calculation of land surface phenology (LSP) metrics such as the start of season (SOS) or end of season (EOS). Overall, from good to high performance was achieved, in particular for the estimation of canopy-level traits such as leaf area index (LAI) and canopy chlorophyll content, with normalized root mean square errors (NRMSE) of 9% and 10%, respectively. By means of the GPR gap-filling time series of S2, entire tiles were reconstructed, and resulting maps were demonstrated over an agricultural area in Castile and Leon, Spain, where crop calendar data were available to assess the validity of LSP metrics derived from crop traits. In addition, phenology derived from the normalized difference vegetation index (NDVI) was used as reference. NDVI not only proved to be a robust indicator for the calculation of LSP metrics, but also served to demonstrate the good phenology quality of the quantitative trait products. Thanks to the GEE framework, the proposed workflow can be realized anywhere in the world and for any time window, thus representing a shift in the satellite data processing paradigm. We anticipate that the produced LSP metrics can provide meaningful insights into crop seasonal patterns in a changing environment that demands adaptive agricultural production.

Diversity in Plant Breeding: A New Conceptual Framework.

Faced with an accelerating rate of environmental change and the associated need for a more sustainable, low-input agriculture, the urgent new challenge for crop science is to find ways to introduce greater diversity to cropping systems. However, there is a dearth of generic formalism in programs seeking to diversify crops. In this opinion, we propose a new framework, derived from ecological theory, that should enable diversity targets to be incorporated into plant-breeding programs. While ecological theory provides criteria for maintaining diversity and optimizing the production of mixtures, such criteria are rarely fully realized in natural ecosystems. Conversely, crop breeding should optimize both agronomic value and the ability of plants to perform and live alongside one another. This framework represents an opportunity to develop more sustainable crops and also a radical new way to apply ecological theory to cropping systems.