Advancing Agricultural Production With Machine Learning Analytics: Yield Determinants for California's Almond Orchards.

Agricultural productivity is subject to various stressors, including abiotic and biotic threats, many of which are exacerbated by a changing climate, thereby affecting long-term sustainability. The productivity of tree crops such as almond orchards, is particularly complex. To understand and mitigate these threats requires a collection of multi-layer large data sets, and advanced analytics is also critical to integrate these highly heterogeneous datasets to generate insights about the key constraints on the yields at tree and field scales. Here we used a machine learning approach to investigate the determinants of almond yield variation in California's almond orchards, based on a unique 10-year dataset of field measurements of light interception and almond yield along with meteorological data. We found that overall the maximum almond yield was highly dependent on light interception, e.g., with each one percent increase in light interception resulting in an increase of 57.9 lbs/acre in the potential yield. Light interception was highest for mature sites with higher long term mean spring incoming solar radiation (SRAD), and lowest for younger orchards when March maximum temperature was lower than 19°C. However, at any given level of light interception, actual yield often falls significantly below full yield potential, driven mostly by tree age, temperature profiles in June and winter, summer mean daily maximum vapor pressure deficit (VPDmax), and SRAD. Utilizing a full random forest model, 82% (±1%) of yield variation could be explained when using a sixfold cross validation, with a RMSE of 480 ± 9 lbs/acre. When excluding light interception from the predictors, overall orchard characteristics (such as age, location, and tree density) and inclusive meteorological variables could still explain 78% of yield variation. The model analysis also showed that warmer winter conditions often limited mature orchards from reaching maximum yield potential and summer VPDmax beyond 40 hPa significantly limited the yield. Our findings through the machine learning approach improved our understanding of the complex interaction between climate, canopy light interception, and almond nut production, and demonstrated a relatively robust predictability of almond yield. This will ultimately benefit data-driven climate adaptation and orchard nutrient management approaches

Albedo on cropland: Field-scale effects of current agricultural practices in Northern Europe

Agricultural land use and management affect land surface albedo and thus the climate. Increasing the albedo of cropland could enhance reflection of solar radiation, counteracting the radiative forcing (RF) of greenhouse gases (GHGs) and local warming. However, knowledge is lacking on how agricultural practices affect albedo under local conditions, and on the benefits of individual practices. In this study, field measurements were made in 15 paired plots at a site in Northern Europe to determine albedo, net shortwave irradiance and RF impacts under various common crops, cultivation intensities and tillage practices. Field data for 2019-2020 were compared with satellite-based albedo for the surrounding region in 2010-2020. At regional level, different combinations of soil type, yearly weather and agricultural practices led to great variability in the albedo of individual crops, despite similar pedo-climatic conditions. At field level within years, albedo differences were determined mainly by crop type, species-specific phenology and post-harvest management. Annual albedo was higher with perennial ley (0.20-0.22) and winter-sown crops (0.18-0.22) than with spring-sown crops (0.16-0.18) and bare soil (0.13). Barley had the highest albedo among winter and spring cereals. In summer, when increased albedo could alleviate local heat stress, oats reduced net shortwave irradiance at the surface by 0.8-5.8 Wm(-2) compared with other cereals, ley, peas or rapeseed. Delayed or reduced tillage gave high local cooling potential (up to-13.6 Wm(-2)) in late summer. Potential benefits for global mean climate as GWP(100 )per hectare and year reached-980 kg CO(2)e for avoiding black fallow,-578 kg CO(2)e for growing a winter-sown variety and-288 kg CO(2)e for delayed tillage. Thus realistic albedo increases on cropland could have important effects on local temperatures and offset a substantial proportion of the RF deriving from field-scale GHG emissions on short time-scales

Active Amplification of the Terrestrial Albedo to Mitigate Climate Change: An Exploratory Study

This study explores the potential to enhance the reflectance of solar insolation by the human settlement and grassland components of the Earth's terrestrial surface as a climate change mitigation measure. Preliminary estimates derived using a static radiative transfer model indicate that such efforts could amplify the planetary albedo enough to offset the current global annual average level of radiative forcing caused by anthropogenic greenhouse gases by as much as 30 percent or 0.76 W/m2. Terrestrial albedo amplification may thus extend, by about 25 years, the time available to advance the development and use of low-emission energy conversion technologies which ultimately remain essential to mitigate long-term climate change. However, additional study is needed to confirm the estimates reported here and to assess the economic and environmental impacts of active land-surface albedo amplification as a climate change mitigation measure.Comment: 21 pages, 3 figures. In press with Mitigation and Adaptation Strategies for Global Change, Springer, N

Effect of Land Management on Grassland Carbon Dioxide Fluxes

Grassland soils can act as both a source and sink for atmospheric carbon dioxide (CO2). Implementing grassland management practices that increase the rates of soil CO2 sequestration are urgently sought to offset Ireland’s agricultural greenhouse gas emissions. However, land management of Irish grasslands is not yet accounted for in the national inventories simultaneously posing a limitation and opportunity for refining modelled estimates of carbon sequestration. In this study, eddy covariance flux towers were established to monitor net ecosystem CO2 exchange (NEE), gross primary productivity (GPP) and ecosystem respiration (Re) in three grassland types (intensive dairy grazing, drystock grazing and zero- grazing) in geographically distinct agricultural catchments in Ireland. The initial results show larger magnitude of NEE, GPP and Re in intensively grazed and zero-grazed grasslands that are subject to frequent grazing/defoliation followed by recovery of photosynthetic potential. The continuously grazed drystock grassland exhibited lower NEE and GPP rates but smaller seasonal fluctuations in daily fluxes which may reflect the reduction in nutrient availability to support higher GPP. However, the drystock grazed grassland had significantly higher soil water content which may stimulate higher soil CO2 respiration resulting in lower NEE over time. Management practices involving defoliation and nutrient supply influenced affected season CO2 exchange but longer-term flux monitoring is required to assess the net ecosystem carbon budgets of each grassland system

Snow-stubble-atmosphere interactions during snowmelt on the Canadian Prairies

Snowmelt is a critical component of the Canadian Prairie hydrological cycle and has significant hydrological and agronomic implications. Within this region, snowmelt can also be a very complicated phenomenon to accurately observe and model due to the occurrence of shallow snowpacks, the unknown energy balance implications of emerging crop stubble during melt on cultivated fields, and the effects of spatiotemporal heterogeneity of snowcover on local-scale advection. The objective of this research was to improve the physical understanding of these complex and interacting processes with deployment of novel observation systems and development and application of new physics-based process models. Intensive field campaigns for the 2015 and 2016 snowmelt season were conducted near Rosthern, Saskatchewan and provided the observations necessary to conduct this research. Application of novel observation systems demonstrated: 1) the ability to remotely-sense maximum prairie snow depth with imagery collected from an unmanned aerial vehicle and processed with Structure from Motion techniques, and 2) the first identification and quantification of latent heat advection from ponded meltwater to snow with development and deployment of a water vapor, air temperature, and wind speed profiling system. Model development resolved: 1) the small scale and dynamic energy balance interactions between the stubble, snow, and atmosphere in a physically based, uncalibrated energy balance model, 2) local-scale sensible and latent heat advection contributions to snowmelt in a modelling framework that facilitates easy coupling to existing one-dimensional snowmelt models, and 3) the influence of stubble upon meltwater partitioning in a coupled model that accounts for snow accumulation, melt and infiltration processes. This study demonstrates that: 1) compensatory interactions with emerging stubble result in negligible differences in the net snow surface energy balance, 2) the inclusion of advection into snow models improves their physical realism and snowmelt predictions, and 3) that the compensatory interactions of stubble on accumulation and melt processes are secondary to the frozen soil infiltration process which is the dominant control on meltwater partitioning. The advances in observational and modelling capacity shown here improve the understanding and predictive capacity of the complex interactions governing the melt processes of prairie snowpacks

Detection and attribution of an anomaly in terrestrial photosynthesis in Europe during the COVID-19 lockdown

Carbon dioxide (CO2) uptake by plant photosynthesis, referred to as gross primary production (GPP) at the ecosystem level, is sensitive to environmental factors, including pollutant exposure, pollutant uptake, and changes in the scattering of solar shortwave irradiance (SWin) - the energy source for photosynthesis. The 2020 spring lockdown due to COVID-19 resulted in improved air quality and atmospheric transparency, providing a unique opportunity to assess the impact of air pollutants on terrestrial ecosystem functioning. However, detecting these effects can be challenging as GPP is influenced by other meteorological drivers and management practices. Based on data collected from 44 European ecosystem-scale CO2 flux monitoring stations, we observed significant changes in spring GPP at 34 sites during 2020 compared to 2015-2019. Among these, 14 sites showed an increase in GPP associated with higher SWin, 10 sites had lower GPP linked to atmospheric and soil dryness, and seven sites were subjected to management practices. The remaining three sites exhibited varying dynamics, with one experiencing colder and rainier weather resulting in lower GPP, and two showing higher GPP associated with earlier spring melts. Analysis using the regional atmospheric chemical transport model (LOTOS-EUROS) indicated that the ozone (O-3) concentration remained relatively unchanged at the research sites, making it unlikely that O-3 exposure was the dominant factor driving the primary production anomaly. In contrast, SWin increased by 9.4 % at 36 sites, suggesting enhanced GPP possibly due to reduced aerosol optical depth and cloudiness. Our findings indicate that air pollution and cloudiness may weaken the terrestrial carbon sink by up to 16 %. Accurate and continuous ground-based observations are crucial for detecting and attributing subtle changes in terrestrial ecosystem functioning in response to environmental and anthropogenic drivers

Detection and attribution of an anomaly in terrestrial photosynthesis in Europe during the COVID-19 lockdown

Carbon dioxide (CO2) uptake by plant photosynthesis, referred to as gross primary production (GPP) at the ecosystem level, is sensitive to environmental factors, including pollutant exposure, pollutant uptake, and changes in the scattering of solar shortwave irradiance (SWin) − the energy source for photosynthesis. The 2020 spring lockdown due to COVID-19 resulted in improved air quality and atmospheric transparency, providing a unique opportunity to assess the impact of air pollutants on terrestrial ecosystem functioning. However, detecting these effects can be challenging as GPP is influenced by other meteorological drivers and management practices. Based on data collected from 44 European ecosystem-scale CO2 flux monitoring stations, we observed significant changes in spring GPP at 34 sites during 2020 compared to 2015–2019. Among these, 14 sites showed an increase in GPP associated with higher SWin, 10 sites had lower GPP linked to atmospheric and soil dryness, and seven sites were subjected to management practices. The remaining three sites exhibited varying dynamics, with one experiencing colder and rainier weather resulting in lower GPP, and two showing higher GPP associated with earlier spring melts. Analysis using the regional atmospheric chemical transport model (LOTOS-EUROS) indicated that the ozone (O3) concentration remained relatively unchanged at the research sites, making it unlikely that O3 exposure was the dominant factor driving the primary production anomaly. In contrast, SWin increased by 9.4 % at 36 sites, suggesting enhanced GPP possibly due to reduced aerosol optical depth and cloudiness. Our findings indicate that air pollution and cloudiness may weaken the terrestrial carbon sink by up to 16 %. Accurate and continuous ground-based observations are crucial for detecting and attributing subtle changes in terrestrial ecosystem functioning in response to environmental and anthropogenic drivers

MODIS: Moderate-resolution imaging spectrometer. Earth observing system, volume 2B

The Moderate-Resolution Imaging Spectrometer (MODIS), as presently conceived, is a system of two imaging spectroradiometer components designed for the widest possible applicability to research tasks that require long-term (5 to 10 years), low-resolution (52 channels between 0.4 and 12.0 micrometers) data sets. The system described is preliminary and subject to scientific and technological review and modification, and it is anticipated that both will occur prior to selection of a final system configuration; however, the basic concept outlined is likely to remain unchanged

Climatic impacts of bushland to cropland conversion in Eastern Africa

Bushlands (Acacia-Commiphora) constitute the largest and one of the most threatened ecosystems in East Africa. Although several studies have investigated the climatic impacts of land changes on local and global climate, the main focus has been on forest loss and the impacts of bushland clearing thus remain poorly understood. Measuring the impacts of bushland loss on local climate is challenging given that changes often occur at fragmented and small patches. Here, we apply high-resolution satellite imagery and land surface flux modeling approaches to unveil the impacts of bushland clearing on surface biophysical properties and its associated effects on surface energy balance and land surface temperature. Our results show that bushland clearing leads to an average reduction in evapotranspiration of 0.4 mm day(-1). The changes in surface biophysical properties affected the surface energy balance components with different magnitude. The reduction in latent heat flux was stronger than other surface energy fluxes and resulted in an average net increase in daytime land surface temperature (LST) of up to 1.75 K. These results demonstrate the important impact of bushland-to-cropland conversion on the local climate, as they reveal increases in LST of a magnitude comparable to those caused by forest loss. This finding highlights the necessity of bushland conservation for regulating the land surface temperature in East Africa and, at the same time, warns of the climatic impacts of clearing bushlands for agriculture. (c) 2020 The Authors. Published by Elsevier B.V.Peer reviewe