Nitrogen doses in topdressing affect vegetation indices and corn yield

Nitrogen is the main nutrient required by corn crop, especially in Cerrado soils. Remote sensing techniques can be used to generate additional information now of nitrogen fertilization recommendation. This work investigated the association of plant height and dry matter phenological variables together with NDVI, REDEDGE, SAVI, and IV 760/550 vegetation indices (VIs) with corn grain yield, under different N doses. Sowing occurred in November 2016, at a spacing of 0.45 m between rows and a 60,000 ha-1 plant population. Four N doses (0, 80, 160, and 240 kg of N ha-1) were applied at phenological stage V4. The experimental design consisted of randomized blocks, containing four N doses in topdressing and 16 replications. The active optical sensor Crop Circle ACS-470 was used to obtain the VIs. The NDVI, SAVI, and RE indices have a high positive association with each other and with the variables plant height and dry matter. Polynomial regression equations were adjusted between the variables in response as doses of N. Afterwards, they were estimated as correlations between variables and results expressed through the network of correlations. Finally, a multivariate analysis of canonical variables was performed to understand the interrelationship between the variables and each dose of N applied. NDVI and RE have a positive relationship of moderate magnitude with grain yield in corn crops

Agricultural and food security impacts from the 2010 Russia flash drought

The flash drought and its associated heat wave that affected western Russia in the summer of 2010 had significant cascading agricultural and socioeconomic impacts. Drought indicators sensitive to soil moisture and evapotranspiration (ET) showed that the flash drought began in June 2010, then intensified rapidly and expanded to cover much of western Russia. By early July, almost all of the major wheat producing regions of Russia were experiencing extreme water stress to the winter and spring wheat crops. The timing of the onset of the flash drought was particularly devastating as the period of most rapid intensification overlapped with the flowering stage for both the winter and spring wheat crops. As a result, wheat yields in Russia were reduced by over 70 percent in top wheat producing oblasts and total wheat production was reduced by 20 million metric tons (MT) compared to the previous seasons. In fulfillment of its recently adopted Food Security Doctrine, the Russian government banned the export of wheat in early August 2010 to preserve wheat for its own consumption. Further compounding matters on a global scale, the significant reduction in wheat production in Russia coincided with wheat production issues in places like western Australia, which led to a large drop in global wheat stocks. The sharp drop in global wheat stocks coincided with a rapid increase in wheat prices across the globe. The rapid increase in wheat prices, partly resulting from the rapid intensification of drought in Russia, led to increased prices for wheat flour and bread in many countries throughout the world. This ultimately led to an increase in poverty and civil unrest in countries like Mozambique and Egypt with a history of inequality and poverty

An Assessment of the Hydrological Trends Using Synergistic Approaches of Remote Sensing and Model Evaluations over Global Arid and Semi-Arid Regions

Drylands cover about 40% of the world’s land area and support two billion people, most of them living in developing countries that are at risk due to land degradation. Over the last few decades, there has been warming, with an escalation of drought and rapid population growth. This will further intensify the risk of desertification, which will seriously affect the local ecological environment, food security and people’s lives. The goal of this research is to analyze the hydrological and land cover characteristics and variability over global arid and semi-arid regions over the last decade (2010–2019) using an integrative approach of remotely sensed and physical process-based numerical modeling (e.g., Global Land Data Assimilation System (GLDAS) and Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) models) data. Interaction between hydrological and ecological indicators including precipitation, evapotranspiration, surface soil moisture and vegetation indices are presented in the global four types of arid and semi-arid areas. The trends followed by precipitation, evapotranspiration and surface soil moisture over the decade are also mapped using harmonic analysis. This study also shows that some hotspots in these global drylands, which exhibit different processes of land cover change, demonstrate strong coherency with noted groundwater variations. Various types of statistical measures are computed using the satellite and model derived values over global arid and semi-arid regions. Comparisons between satellite- (NASA-USDA Surface Soil Moisture and MODIS Evapotranspiration data) and model (FLDAS and GLDAS)-derived values over arid regions (BSh, BSk, BWh and BWk) have shown the over and underestimation with low accuracy. Moreover, general consistency is apparent in most of the regions between GLDAS and FLDAS model, while a strong discrepancy is also observed in some regions, especially appearing in the Nile Basin downstream hyper-arid region. Data-driven modelling approaches are thus used to enhance the models’ performance in this region, which shows improved results in multiple statistical measures ((RMSE), bias (ψ), the mean absolute percentage difference (|ψ|)) and the linear regression coefficients (i.e., slope, intercept, and coefficient of determination (R2))

Earth Observations and Integrative Models in Support of Food and Water Security

Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries

Intercomparison of Soil Moisture, Evaporative Stress, and Vegetation Indices for Estimating Corn and Soybean Yields Over the U.S.

This paper presents an intercomparative study of 12 operationally produced large-scale datasets describing soil moisture, evapotranspiration (ET), and or vegetation characteristics within agricultural regions of the contiguous United States (CONUS). These datasets have been developed using a variety of techniques, including, hydrologic modeling, satellite-based retrievals, data assimilation, and survey in-field data collection. The objectives are to assess the relative utility of each dataset for monitoring crop yield variability, to quantitatively assess their capacity for predicting end-of-season corn and soybean yields, and to examine the evolution of the yield-index correlations during the growing season. This analysis is unique both with regards to the number and variety of examined yield predictor datasets and the detailed assessment of the water availability timing on the end-of-season crop production during the growing season. Correlation results indicate that over CONUS, at state-level soil moisture and ET indices can provide better information for forecasting corn and soybean yields than vegetation-based indices such as normalized difference vegetation index. The strength of correlation with corn and soybean yields strongly depends on the interannual variability in yield measured at a given location. In this case study, some of the remotely derived datasets examined provide skill comparable to that of in situ field survey-based data further demonstrating the utility of these remote sensing-based approaches for estimating crop yield

Improving the drought risk assessment and preparedness for winter wheat farming in Oklahoma

Droughts pose a persistent threat to agriculture in the Southern Great Plains (SGP). Oklahoma is a major contributor to dryland winter wheat farming in the SGP, acrop that is highly susceptible to drought episodes. Modern tools of environmental monitoring and crop simulations provide great opportunity to improve agricultural drought risk assessment and preparedness. However, in the wheat belt of Oklahoma, these modern technologies have been less utilized to understand the dynamics of droughtand its effects on winter wheat growth. There is an immediate need to investigate the prospective of advanced environmental monitoring networks and practitioner-oriented crop models to mitigate the impacts of dry periods on crop yield. The objectives of this study were to: 1) develop a new drought index using soil moisture and weather data for improved drought monitoring of winter wheat; 2) calibrate and validate a crop model and employ it to study the impacts of planting date and water availability at planting on theyield of dryland and irrigated winter wheat; and 3) apply the calibrated crop model across the winter wheat belt in Oklahoma to investigate the spatial variation in yield and itsdrought sensitivity. The development of a new drought index showed that soil moisture information in conjunction with reference evapotranspiration can improve the estimation of drought magnitude. Also, the new drought index correlated well with the winter wheat yield, showing its potential for agricultural drought monitoring for Oklahoma. Long-term crop modeling study for winter wheat in Oklahoma revealed that October planting dates usually provide better yields in comparison to September sowing. Moreover, the considerable impact of soil moisture at the time of sowing was noted on overall wheat yields, and the irrigation had noticeable positive effect on yield, especially in drought years. Gridded crop modeling helped understanding the spatial variation in potential dryland yields in wheat belt of Oklahoma. Furthermore, it was found that the winter wheat yield was highly correlated to drought in the months of March to May, and West Central climate division was highly sensitive to dry periods in Oklahoma

Drought impacts assessment in Brazil - a remote sensing approach

Climate extremes are becoming more frequent in Brazil; studies project an increase in drought occurrences in many regions of the country. In the south, drought events lead to crop yield losses affecting the value chain and, therefore, the local economy. In the northeast, extended periods of drought lead to potential land degradation, affecting the livelihood and hindering local development. In the southern Amazon, an area that experienced intense land use change (LUC) in the last, the impacts are even more complex, ranging from crop yield loss and forest resilience loss, affecting ecosystem health and putting a threat on the native population traditional way of living. In the studies here we analyzed the drought impacts in these regions during the 2000s, which vary in nature and outcomes. We addressed some of the key problems in each of the three regions: i) for the southern agriculture, we tackled the problem of predicting soybean yield based on within-season remote sensing (RS) data, ii) in the northeast we mapped areas presenting trends of land degradation in the wake of an extended drought and, iii) in southern Amazon, we characterized a complex degradation cycle encompassing LUC, fire occurrence, forest resilience loss, carbon balance, and the interconnectedness of these factors impacting the local climate. Advisor: Brian D. Wardlo

Enhancing regional estimates of evapotranspiration with earth observation data

Food security and food sustainability are high on the global policy agenda. Reliable information on crop water use and terrestrial water stress are important to ensure an optimal use of available water resources and for enhancing crop production. Remote sensing provides a feasible avenue to estimate regional evapotranspiration (ET), which can be employed to assess terrestrial water stress. However, the heterogeneity of land surfaces and the accumulated errors from various inputs often result in substantial biases in most global or regional ET models across different landscapes. Reducing uncertainties in available ET products or remote sensing (RS)-based models and obtaining regional ET estimates with improved accuracy is important for effectively using ET to support agricultural monitoring and water resources managements. This thesis first compared different Priestly-Taylor (PT)-based methods that use three Earth observation-based alternatives - apparent thermal inertia (ATI), microwave soil moisture (SM), and optical spectral indices based on shortwave infrared (SWIR) to assess soil evaporation over cropland and grassland regions. Using FLUXNET data as ET reference, the results illustrated that the incorporation of the SWIR-based soil moisture divergence index (SMDI) and microwave-based SM into monthly soil evaporation led to 6% and 5% increase in explained ET variances and reduced RMSE by 23.2% and 13.1% for cropland and grassland, respectively, as compared to PT-JPL using atmospheric reanalysis data only. The results suggested that a combination of optical SWIR and microwave SM has good potential to improve the PT-JPL model accuracy for agricultural landscapes. Based on the performance of different PT-based methods, ET estimates derived from the revised PT method were used to assess water budgets across 53 catchments in central-western Europe with a humid climate and were compared with three additional ET data sources (MOD16, GLEAM, and PT-JPL). Surprisingly, all RS-based ET estimates significantly diverged from water balance-based ET (ETWB) in 45 humid catchments, whereas most previous studies that focussed on arid catchments or on the global scale found significantly less divergence. Using ET retrievals from the Budyko framework and upscaled ET from FLUXCOM as references, the closure errors of water budgets were sensitive to errors arising from precipitation data in humid regions and the water balance approach was found to overestimate ET during heavy rainfall events. Instead, the Budyko framework that describes the partitioning of precipitation to ET as a functional balance between atmospheric water supply (precipitation, P) and demand (potential evapotranspiration, PET) had good correlation with ET ensemble from multiple data sources and upscaled ET from FLUXCOM product. 161 Summary The results indicated that errors from precipitation and terrestrial water storage anomalies introduce large uncertainties in ETWB, thereby complicating water balance validation in humid regions across multiple timesteps. To improve the application of ETWB for benchmarking ETEB in humid regions, high-quality input data should be used or – like the Budyko framework – energy constraints should be considered. The thesis then proceeds to explore causes for the notable deviations between observed and Budyko-predicted water balances in certain catchments. The results revealed that for humid catchments, topography and seasonal cumulative moisture surplus can explain the spatial distributions of Budyko scatter with r higher than 0.65, whereas soil properties and vegetation indices explained little of the variance (r≤0.30). Temporally, the interannual variability of Budyko scatter was negatively correlated with annual average vegetation indices, particularly for catchments with relatively low vegetation cover. This thesis provides valuable insights to the interpretation of the Budyko framework and offers possible solutions to improve its performance to predict the spatiotemporal variability of water balances. Lastly, to address the deviations from the predictive Budyko curve, additional controls of hydrological partitioning were introduced to correct Budyko scatter between catchments and between years. The results illustrated that the use of catchment climatic seasonality properties and topography attributes is effective in reproducing the Budyko parameter (w) with an r of 0.76 and RMSE of 0.49 for all 45 catchments in central-western Europe. After the correction of temporal Budyko scatter using interannual variability of vegetation information and the fraction of precipitation falling as snow, the performance of the modified Budyko-type equation improves with respect to the original Budyko framework, in comparison to ETWB at catchment scale (∆r of 0.26 and ∆RMSE of 19.19 mm/yr). When compared with the gridded ET ensemble using energy balance, the enhanced Budyko framework is generally effective to reproduce the spatial distribution of ET with good similarity, even in ungauged regions. Overall, the revised Budyko framework shows improved performance in predicting water balances and can be applied to assess crop water use and terrestrial water stress at regional scale, particularly in ungauged areas. Overall, this thesis contributes significantly to the enhancement of regional ET estimation using Earth observation. It proposes a novel blended parameterization for soil moisture constraints in the modified PT-JPL model, which is capable of capturing the soil evaporation more accurately within agroecosystems. Meanwhile, this thesis proposes a new water balance-based validation method that uses the Budyko framework integrated with environmental parameters. By developing improved RS-based models and water balance-based validation methods, this thesis provides valuable insights into the complexities of ET 162 Summary estimation at the regional scale. These findings are expected to advance the application of ET in decision-making regarding the management of agriculture and water resources

Using a surface energy budget framework to characterize grass-biophysical response to changes in climate in support of on-farm decision making in Ireland

xxiv Abstract This thesis, for the first time in Ireland, uses a framework that combines a land surface scheme (LSS) based on a surface energy budget theory, available environmental observations, land surface and atmospheric analyses, to understand essential mechanistic factors that determine grass growth response across the Irish landscape. A soil moisture model parameter (C soil) is identified as the key factor that distinguishes soil types and their ability to retain water for plant growth, plant response to exchange processes, and drives the response of LSS in drying soils. A Modification of this parameter indicates that the LSS can be transferred to other locations. In the context of understanding the links between land surface dynamic processes and the persistence of 2018 summer drought regionally, drying soils and high atmospheric anomalies result in a reduced evapotranspiration (ET) process. This is the situation over grasslands in the east and south east of the country where a wet ‘evaporative’ regime quickly shifts into a ‘transitional’ regime in which vegetation functioning and ET are controlled by soil water availability. Particularly, a threshold value of soil moisture content that suggests the onset of 2018 agricultural drought has been found across the regions. The importance of water use efficiency for monitoring grass growth at field level and for distinguishing zones of optimum productivity is further discussed in the thesis. Overall, the findings demonstrate the potential consequences of climate change on Irish grasslands and the need for policies that are tailored to reinforcing observation networks to complement theories and model outputs akin to on-farm adaptation and optimization of water availability and productivity