Solar energy development is land intensive and recent studies have demonstrated the negative impacts of large-scale solar deployment on vegetation and soil. Co-locating vegetation with managed grazing on utility scale solar PV sites could provide a sustainable solution to meeting the growing food and energy demands, along with providing several co-benefits. However, the impacts of introducing grazing on soil properties at vegetated solar PV sites are not well understood. To address this knowledge gap, we investigated the impacts of episodic sheep grazing on soil properties (micro and macro nutrients, carbon storage, soil grain size distribution) at six commercial solar PV sites (MN, USA) and compared that to undisturbed control sites. Results indicate that implementing managed sheep grazing significantly increased total carbon storage (10-80%) and available nutrients, and the magnitude of change correlated with the grazing frequency (1-5 years) at the study sites. Furthermore, it was found that sites that experienced consecutive annual grazing treatments benefitted more than intermittently grazed sites. The findings will help in designing resource conserving integrated solar energy and food/fodder systems, along with increasing soil quality and carbon sequestration.
Simulations of crop yield due to climate change vary widely between models, locations, species, management strategies, and Representative Concentration Pathways (RCPs). To understand how climate and adaptation affects yield change, we developed a meta-model based on 8703 site-level process-model simulations of yield with contrasting future adaptation strategies and climate scenarios for maize, rice, wheat and soybean. We tested 10 statistical models, including some machine learning models, to relate the percentage change in future yield relative to the baseline period (2000-2010) to explanatory variables related to adaptation strategy and climate change. We used the best model to produce global maps of yield change for the RCP4.5 scenario and identify the most influential variables affecting yield change using Shapley additive explanations. For most locations, adaptation was the most influential factor determining the yield change for maize, rice and wheat. Without adaptation under RCP4.5, all crops are expected to experience average global yield losses of 6–21%. Adaptation alleviates this average loss by 1–13%. Maize was most responsive to adaptive practices with a mean yield loss of -21 % [range across locations: -63%, +3.7%] without adaptation and -7.5% [range: -46%, +13%] with adaptation. For maize and rice, irrigation method and cultivar choice were the adaptation types most able to prevent large yield losses, respectively. When adaptation practices are applied, some areas may experience yield gains, especially at northern high latitudes. These results reveal the critical importance of implementing adequate adaptation strategies to mitigate the impact of climate change on crop yields.
Injecting manure and commercial fertilizer beneath the soil surface is an important nutrient management practice that conserves ammonia-nitrogen (N) but creates distinct bands of N below the soil surface. To date, no widely accepted soil nitrate sampling protocol has been developed to account for the extreme heterogeneity created by injection. To develop sampling recommendations for Pre-Sidedress Nitrate Test (PSNT), we quantified patterns of NO3–N concentrations in soil from of corn (Zea mays L) plots injected with liquid dairy cattle (Bos taurus L) manure at 76 cm spacing over two years. Soil monoliths were collected to allow precise sampling of 30 cm deep by 2.5 cm soil cores from which a mid-season PSNT was determined. Monte Carlo simulation was conducted to simulate the effects of alternative soil sampling protocols on bias and error. Results from the simulation support the following equispaced sampling protocol: five, 30-cm deep soil cores are spaced 15 cm apart and oriented in a line perpendicular to the injected manure bands, collected at four locations in the field, to produce a single composite of 20 samples for NO3- analysis. It is not necessary to know manure band location. As spatially discrete manure application patterns become more prevalent with the expansion of manure injection, we believe this PSNT sampling protocol balances risk of error with practical concerns needed to promote adoption.
Properly designed, calibrated, and operated weighing lysimeters are recognized as accurate tools for measuring changes of soil water storage (ΔS) in the soil profile contained in the lysimeter. The neutron probe, NP, also is recognized as an accurate tool for determining soil profile water storage, S, and ΔS over the depth range of the neutron probe readings, again when properly calibrated and operated. Both methods have been used to calculate evapotranspiration (ET) using the soil water balance equation (ET = ΔS + I + P + R + F) applied to a control volume, where I is irrigation, P is precipitation, R is the sum of runon and runoff, and F is flux into or out of the control volume. However, weighing lysimeters are expensive to install and operate and are not portable, and the neutron probe faces regulatory pressures and cannot be used unattended, limiting its use. Past attempts to use electromagnetic soil water sensors based on capacitance principles to accurately determine profile water content have not met with success. Recent advances in soil water senor technology have led to accurate, low power, and relatively inexpensive electronic soil water sensors based on time domain reflectometry (TDR) theory, which can be installed in situ. Assuming that accurate values or controls were available for I, P, R, and F, we concentrated on evaluating S and ΔS as determined by lysimeter, NP, and TDR sensors. Over a cropping season at Bushland, Texas, USA we compared S and ΔS in a 2.3-m deep profile of silty clay loam soil as assessed by a large, precision weighing lysimeter, the NP in two access tubes in the lysimeter, and three profiles of TDR soil water sensors installed in the lysimeter, each profile consisting of 15 sensors. Weighing lysimeter mass was recorded every 5 minutes and TDR sensors were read every 15 minutes, both automatically using dataloggers, while the neutron probe readings were done manually at approximately one-week intervals. Comparing TDR sensors with neutron probe, coefficients of determination for profile water content and for ΔS were 0.97 and 0.91, respectively, when one-week intervals were considered. Coefficients of determination for comparisons of TDR sensors to lysimeter were 0.95 for S and 0.92 for ΔS, while values for comparison of neutron probe to lysimeter were 0.91 for water storage and 0.83 for ΔS, again for one-week intervals. Poorer performance of the NP was likely due to the fact that it could not be read to depths greater than 1.90 m, which limited the profile sensed to the top 2.0 m of soil.
Recent land use changes associated with climatic suitability loss in some Coffea arabica growing regions in Guatemala have promoted an accelerated assessment of ecosystem services provided by shade coffee plantations. Yet, the different management schemes operating this heterogeneous landscape limit our ability to extrapolate these findings. In addition to climate suitability loss, economical constrains and pests and infestations have promoted an overall loss of ecosystem services associated to these agroforestry systems, some of which have not yet been accounted for. Among these ecosystem services, carbon stored in shade coffee plantations has not been estimated for most of the coffee growing regions in Guatemala at a site-specific level nor have specific allometric equations being developed to include the stumps left behind after coffee has been pruned, resulting in an underestimation of carbon content in these systems. In this study, we estimated carbon content in living biomass above and below-ground in 36 different coffee farms along the Sierra Madre of Guatemala. We developed allometric equations for each dominant shade tree species and one for coffee plants (Coffea arabica). Remaining plant biomass and carbon stock was estimated using previously published equations. On average, the agroforestry plots contained 80.5 ± 5.2 t C / ha. In these plots, shade trees accounted for most of the carbon content (45%) followed by soil (40%). Coffee plants represented 9% of the carbon in the system, indicating that a coffee system with little or no shade has very low potential for carbon allocation. Coffee farms under a sustainability certification scheme were not statistically different than the non-certified farms. However, the effect of certification on the carbon content should continue to be explored in future research.
We conduct the first 4D-Var inversion of NH3 accounting for NH3 bidirectional flux, using CrIS satellite NH3 observations over Europe in 2016. We find posterior NH3 emissions peak more in springtime than prior emissions at continental to national scales, and annually they are generally smaller than the prior emissions over central Europe, but larger over most of the rest of Europe. Annual posterior anthropogenic NH3 emissions for 25 European Union members (EU25) are 25% higher than the prior emissions and very close(<2% difference) to other inventories. Our posterior annual anthropogenic emissions for EU25, the UK, the Netherlands, and Switzerland are generally 10-20% smaller than when treating NH3 fluxes as uni-directional emissions, while the monthly regional difference can be up to 34% (Switzerland in July). Compared to monthly mean in-situ observations, our posterior NH3 emissions from both schemes generally improve the magnitude and seasonality of simulated surface NH3 and bulk NHx wet deposition throughout most of Europe, whereas evaluation against hourly measurements at a background site shows the bi-directional scheme better captures observed diurnal variability of surface NH3. This contrast highlights the need for accurately simulating diurnal variability of NH3 in assimilation of sun-synchronous observations and also the potential value of future geostationary satellite observations. Overall, our top-down ammonia emissions can help to examine the effectiveness of air pollution control policies to facilitate future air pollution management, as well as helping us understand the uncertainty in top-downNH3emission estimates associated with treatment of NH3surface exchange.
Ground-based visual assessments of co-occurring foliar diseases are time-consuming, laborious, and subjective due to the spatiotemporal overlapping of different lesion types and patterns. We took advantage of this scenario to explore the feasibility of unmanned aircraft systems (UAS)-derived multispectral vegetation indices to measure the variable incidence and severity of a mix of diseases. We rated separately the disease severity (as percent DLA or AUDPC) of artificially inoculated northern leaf blight (NLBart) along with naturally occurring northern leaf spot (NLSnat) and anthracnose leaf blight (ALBnat) in near-isogenic inbred (NILinbreds) and single-cross hybrid (NILhybrids) lines in Aurora, NY in 2018 ad 2019. NLBart and ALBnat were also scored in a contiguous field with a population of maize hybrids with broad genetic base. Total disease severity (tDSground) was estimated from the sum of the scored diseases. Disease severity and grain yield (GYground) were recorded from replicated 2-row plots. Two or three asynchronous UAS flights (no overlapping with ground-based visual estimates of each disease severity) were conducted in each crop season and plot-level vegetation indices (VIsair) were extracted from UAS-derived orthomosaics. Goodness of fit (R^2) between VIsair and tDSground were low (0-0.3) in the three germplasm groups. R^2 values between GYground and VIsair were higher (0.2-0.8) than those between GYground and tDSground (0.1-0.4). Our preliminary results highlight the challenges of dealing with a realistic field situation where the uncertain dynamics of a mix of pathogens and the contrasting perspectives (air vs. ground) involved in the disease screening add complexity that needs to be studied.
The potential of genomic selection (GS) to increase the efficiency of breeding programs has been clearly demonstrated; however, the implementation of GS in rice (Oryza sativa L.) breeding programs has been limited. In recent years, we have begun to work towards implementing GS into the LSU AgCenter rice breeding program. One of the first steps for successful GS implementation is to establish a suitable marker set for the target germplasm and a reliable, cost-effective genotyping platform capable of providing informative marker data with an adequate turnaround time. In this study, we develop an optimized a marker set, the LSU500, for application of routine GS in Southern U.S. rice germplasm. The utility of the LSU500 was demonstrated using four years of breeding data across 8,473 experimental lines and four elite bi-parental populations. The predictive ability of GS ranged from 0.13 to 0.78 for key traits across different market classes and yield trials. Comparisons between phenotypic selection and GS within bi-parental populations using the LSU500 provided evidence of the potential of GS to improve the efficiency of a rice breeding program. The design of this marker set followed a continuous integration strategy, whereby GS is initially introduced into a breeding program while technical and strategic aspects of GS implementation are evaluated, optimized, and integrated into the breeding pipeline on-the-go. The LSU500 marker set has been established through the genotyping service provider Agriplex Genomics, and in the future, it will undergo improvements to reduce the cost and increase the accuracy of GS.
The traditional method of measuring the lettuce height is a manual measurement with instruments, which is greatly affected by human error.At present, researchers have proposed to use color cameras to obtain RGB images of lettuce, and to obtain the height of lettuce from the images. However, these tasks usually require camera calibration or a reference object with a known height, which is somewhat restrictive. Considering that deep neural networks have a powerful ability to feature extraction and expression, without camera calibration and reference objects, we try to use four networks of image recognition to explore the effect of deep learning on abstracting the lettuce height from RGB images. On the test set, including 80 images and height from 0.9 cm to 7.5 cm, we achieve a good result with a mean absolute error of 1.22 mm.
Production of food in space via photosynthetic crops similar to the ones we use on Earth requires profligate use of resources that will be in short supply in early long duration space missions. We demonstrate that production of bulk calories via chemotrophic single cell organisms is 2 to 3 orders of magnitude less expensive in terms of energy, volume, and water usage than via photosynthetic crops. In addition we survey the history and current state of the art in production of food from non-photosynthetic single cell organisms.
Tocochromanols (vitamin E) are an essential part of the human diet. Plant products including maize grain are the major dietary source of tocochromanols; therefore, breeding maize with higher vitamin content (biofortification) could improve human nutrition. Incorporating exotic germplasm in maize breeding for trait improvement including biofortification is a promising approach and an important research topic. However, information about genomic prediction of exotic-derived lines using available training data from adapted germplasm is limited. In this study, genomic prediction was systematically investigated for nine tocochromanol traits within both an adapted (Ames Diversity Panel) and an exotic-derived (BGEM) maize population. While prediction accuracies up to 0.79 were achieved using gBLUP when predicting within each population, genomic prediction of BGEM based on an Ames Diversity Panel training set resulted in low prediction accuracies. Optimal training population (OTP) design methods FURS, MaxCD, and PAM were adapted for inbreds and, along with the methods CDmean and PEVmean, often improved prediction accuracies compared to random training sets of the same size. When applied to the combined population, OPT designs enabled successful prediction of the rest of the exotic-derived population. Our findings highlight the importance of leveraging genotype data in training set design to efficiently incorporate new exotic germplasm into a plant breeding program.
Drought Early Warning Systems (DEWSs) aim to spatially monitor and forecast risk of water shortage to inform early, risk-mitigating interventions. However, due to the scarcity of in-situ monitoring in groundwater-dependent arid zones, spatial drought exposure is inferred using maps of satellite-based indicators such as rainfall anomalies, soil moisture and vegetation indices. On the local scale, these coarse-resolution proxy indicators provide a poor inference of groundwater availability. The improving affordability and technical capability of modern sensors significantly increases the feasibility of taking direct groundwater level measurements in data-scarce, arid regions on a larger scale. Here, we assess the potential of in-situ monitoring to provide a localized index of hydrological drought in Somaliland. We find that calibrating a lumped groundwater model with a short time series of high-frequency groundwater level observations substantially improves the quantification of local water availability when compared to satellite-based indices over the same validation period. By varying the calibration length between 1-30 weeks, we find that data collection beyond 5 weeks adds little to model calibration at all three wells. This suggests that a short monitoring campaign is suitable to improve estimations of local water availability during drought, and provide superior performance compared to regional-scale satellite-based indicators. A short calibration period has practical advantages, as it allows for the relocation of sensors and rapid characterization of a large number of wells. A monitoring system with this contextualized, local information can support earlier financing and better targeting of early actions than regional DEWSs.
Chihuahua State, in northwestern Mexico, has had strong growth in its irrigated agriculture, going from 2010 – 2018 of 461,099 to 597,222 ha. Although the total area planted has decreased due to often droughts. Main growth has occurred in the Chihuahuan desert area, through groundwater extraction, causing aquifers’ overexploitation. This unsustainable growth will provoke a collapse in the management of water resources. This work shows a methodology to determine the current agricultural frontier and monitor the evolution of the irrigated area. Methodology employs the Google Earth Engine (GEE) platform to obtain and process satellite images, and was applied in the Laguna de Hormigas aquifer, located in the Chihuahuan desert area. For this aquifer, 10.55 times its recharge is extracted. Methodology uses Sentinel 2, Landsat 5, and Landsat 8 satellite images. To identify the agricultural frontier, we use Sentinel 2 images (level 1C) with cloud cover less than 10% from 2015-2020. The agricultural frontier is obtained by classifying two bands, one with maximum vegetative development and another with maximum humidity in each pixel. The bands are generated from a statistical historical analysis of the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI). To monitor the evolution of irrigated areas from 2000 – 2020, Landsat 5 and Landsat 8 images of level 2 collection 2 are processed. The agricultural frontier defines the area where growth occurs. From Landsat images, for each year one image with maximum vegetative development is obtained which is processed to show if it has cultivated areas or not. Considering that cultivated areas have an NDVI value greater than 0.2. Results shows that agricultural development in the Laguna de Hormigas aquifer starts in 2010, but from 2016 is accelerated. Results are consistent with the statistics published in the Agrifood and Fisheries Information Service. The methodology developed is useful to analyze the spatio-temporal evolution of agricultural areas in arid regions and can be used in similar zones, without special computational requirements because it uses databases and cloud tools.
Earth kilns are still used for charcoal production in the Eastern Mediterranean and worldwide. Until 2016, around 1,600 tons of charcoal were produced in Israel and the Palestinian territories in about 400 traditional earth kilns that were operated in about the same manner for the last 400 years. The intense air pollution caused by this indigenous practice resulted in higher mortality rates among the workers and the population living close to the charcoal production sites. The air pollution was found to migrate beyond 50 km, causing cross-boundary pollution in Jordan. Since the charcoal production industry processes surplus wood into solid fuel, which is used for heating and cooking, it was imperative to shift this industry to a new type of non-polluting charcoal production system. To upgrade this industry to 21st century standards development and implementation of a new ecological retort system (ERS), became possible through a combined effort by Israeli researchers and Palestinian manufacturers. Comparing the ERS to the old earth kilns suggests that the wood-to-charcoal transformation efficiency is about 10% higher in the ERS and the process duration is half a day vs. about three weeks in a traditional kiln. Generally, ERS is about two orders of magnitude more productive than the traditional earth kilns. The ERS combines a simple operational scheme and higher charcoal yield than a traditional kiln, leading to an increase in the revenue to the charcoal makers, also through byproducts bearing economic value such as electric energy and wood vinegar.
There is a growing consensus on a need for comparing the cropland with a reference state or native land in a prime soil health state to determine soil health management goals in croplands. However, the complex soil heterogeneity and climate variations make soil health potential variable and confound the land-use and management practices while comparing soils from different sites. Identifying a discrete landmass unit where all soils have similar health potential will be instrumental in conducting meaningful comparative studies. This methodological paper proposes and discusses a land unit, Reference Ecological Unit (REU), that accounts for soil and climate variabilities and covers the area with similar soil health potential. The REU is developed for one Major Land Resource Area in Nebraska based on the USDA-NRCS hierarchical land classification system. A true difference in soil health for different land use and agronomic management practices such as no-till and cover crops can be determined by comparing sites within an individual REU. Evaluation of management effects on soil health indicators in an REU will adequately illustrate the beneficial impact of such practices without being confounded by agroecological variations.
Global use of reactive nitrogen (N) has increased over the past century to meet growing food and biofuel demand, while contributing to substantial environmental impacts. To project future N inputs for crop production, many studies assumed that Nitrogen Use Efficiency (NUE) remains the same as the current level under a Business-As-Usual (BAU) scenario. This assumption ignores potential NUE changes caused by shifting crop mixes and the diminishing return of yield increase to N inputs at a given level of technology and management practices (TMP). To evaluate the impacts of these two factors on the projection of future N inputs, we developed and tested three approaches, namely “Same NUE”, “Same TMP”, and “Improving TMP”. We found that the approach considering the diminishing returns in yield response (“Same TMP”) resulted in 268 Tg N yr-1 of N inputs which were 61 and 48 Tg N yr-1 higher when keeping NUE at the current level with and without considering crop mix, respectively. If TMP is assumed to continue to evolve at the pace of past five decades, the projected N inputs reduce to 204 Tg N yr-1, but still 59 Tg N yr-1 higher than the inputs in the baseline year 2006. Overall, our results suggest that the BAU approach that assumes constant NUE may be too optimistic in projecting N inputs, and the full range of projection assumptions need to be carefully explored when investigating future N budgets.
In a model simulating dynamics of a system, parameters can represent system sensitivities and unresolved processes, therefore affecting model accuracy and uncertainty. Taking a light use efficiency (LUE) model as an example, which is a typical approach to estimate gross primary productivity (GPP), we propose a Simultaneous Parameter Inversion and Extrapolation approach (SPIE) to overcome issues stemming from plant-functional-type(PFT)-dependent parameterizations. SPIE refers to predicting model parameters using an artificial neural network based on collected variables, including PFT, climate types, bioclimatic variables, vegetation features, atmospheric nitrogen and phosphorus deposition and soil properties. The neural network was optimized to minimize GPP errors and constrain LUE model sensitivity functions. We compared SPIE with 11 typical parameter extrapolating methods, including PFT- and climate-specific parameterizations, global and PFT-based parameter optimization, site-similarity, and regression approaches. All methods were assessed using Nash-Sutcliffe model efficiency(NSE), determination coefficient and normalized root mean squared error, and contrasted with site-specific calibrations. Ten-fold cross-validated results showed that SPIE had the best performance across sites, various temporal scales and assessing metrics. None of the approaches performed similar to site-level calibrations(NSE=0.95), but SPIE was the only approach showing positive NSE(0.68). The Shapley value, layer-wise relevance and partial dependence showed that vegetation features, bioclimatic variables, soil properties and some PFTs are determining parameters. The proposed parameter extrapolation approach overcomes strong limitations observed in many standard parameterization methods. We argue that expanding SPIE to other models overcomes current limits and serves as an entry point to investigate the robustness and generalization of different models.
While soybeans are among the most consumed crops in the world, the majority of its production lies in hotspot regions in the US, Brazil and Argentina. The concentration of soybean growing regions in the Americas render the supply chain vulnerable to regional disruptions. In the year of 2012 anomalous hot and dry conditions occurring simultaneously in these regions led to low soybean yields, which drove global soybean prices to all-time records. Climate change has already negatively impacted agricultural systems, and this trend is expected to continue in the future. In this study we explore climate change impacts on simultaneous extreme crop failures as the one from 2012. We develop a hybrid model, coupling a process-based crop model with a machine learning model, to improve the simulation of soybean production. We assess the frequency and magnitude of events with similar or higher impacts than 2012 under different future scenarios, evaluating anomalies both with respect to present day and future conditions to disentangle the impacts of (changing) climate variability from the long-term mean trends. We find the long-term trends of mean climate increase the occurrence and magnitude of 2012 analogue crop yield losses. Conversely, anomalies like the 2012 event due to changes in climate variability show an increase in frequency in each country individually, but not simultaneously across the Americas. We deduce that adaptation of the crop production practice to the long-term mean trends of climate change may considerably reduce the future risk of simultaneous soybean losses across the Americas.