Soil carbon sequestration in global working lands as a gateway for negative emission technologies.

The ongoing climate crisis merits an urgent need to devise management approaches and new technologies to reduce atmospheric greenhouse gas concentrations (GHG) in the near term. However, each year that GHG concentrations continue to rise, pressure mounts to develop and deploy atmospheric CO2 removal pathways as a complement to, and not replacement for, emissions reductions. Soil carbon sequestration (SCS) practices in working lands provide a low-tech and cost-effective means for removing CO2 from the atmosphere while also delivering co-benefits to people and ecosystems. Our model estimates suggest that, assuming additive effects, the technical potential of combined SCS practices can provide 30%-70% of the carbon removal required by the Paris Climate Agreement if applied to 25%-50% of the available global land area, respectively. Atmospheric CO2 drawdown via SCS has the potential to last decades to centuries, although more research is needed to determine the long-term viability at scale and the durability of the carbon stored. Regardless of these research needs, we argue that SCS can at least serve as a bridging technology, reducing atmospheric CO2 in the short term while energy and transportation systems adapt to a low-C economy. Soil C sequestration in working lands holds promise as a climate change mitigation tool, but the current rate of implementation remains too slow to make significant progress toward global emissions goals by 2050. Outreach and education, methodology development for C offset registries, improved access to materials and supplies, and improved research networks are needed to accelerate the rate of SCS practice implementation. Herein, we present an argument for the immediate adoption of SCS practices in working lands and recommendations for improved implementation.

Tree-level almond yield estimation from high resolution aerial imagery with convolutional neural network.

Introduction: Estimating and understanding the yield variability within an individual field is critical for precision agriculture resource management of high value tree crops. Recent advancements in sensor technologies and machine learning make it possible to monitor orchards at very high spatial resolution and estimate yield at individual tree level. Methods: This study evaluates the potential of utilizing deep learning methods to predict tree-level almond yield with multi-spectral imagery. We focused on an almond orchard with the 'Independence' cultivar in California, where individual tree harvesting and yield monitoring was conducted for ~2,000 trees and summer aerial imagery at 30cm was acquired for four spectral bands in 2021. We developed a Convolutional Neural Network (CNN) model with a spatial attention module to take the multi-spectral reflectance imagery directly for almond fresh weight estimation at the tree level. Results: The deep learning model was shown to predict the tree level yield very well, with a R2 of 0.96 (±0.002) and Normalized Root Mean Square Error (NRMSE) of 6.6% (±0.2%), based on 5-fold cross validation. The CNN estimation captured well the patterns of yield variation between orchard rows, along the transects, and from tree to tree, when compared to the harvest data. The reflectance at the red edge band was found to play the most important role in the CNN yield estimation. Discussion: This study demonstrates the significant improvement of deep learning over traditional linear regression and machine learning methods for accurate and robust tree level yield estimation, highlighting the potential for data-driven site-specific resource management to ensure agriculture sustainability.

Complete Genomic Sequences of Three Salmonella enterica subsp. enterica Serovar Muenchen Strains from an Orchard in San Joaquin County, California.

We present here the complete genome sequences of three Salmonella enterica subsp. enterica serovar Muenchen strains, LG24, LG25, and LG26. All three strains were isolated from almond drupes grown in an orchard in San Joaquin County, California, in 2016. These genomic sequences are nonidentical and will contribute to our understanding of S. enterica genomics.

Intensive fertilizer use increases orchard N cycling and lowers net global warming potential.

Nitrogen (N) fertilizer use has simultaneously increased global food production and N losses, resulting in degradation of water quality and climate pollution. A better understanding of N application rates and crop and environmental response is needed to optimize management of agroecosystems. Here we show an orchard agroecosystem with high N use efficiency promoted substantial gains in carbon (C) storage, thereby lowering net global warming potential (GWP). We conducted a 5-year whole-system analysis comparing reduced (224 kg N ha-1 yr-1) and intensive (309 kg N ha-1 yr-1) fertilizer N rates in a California almond orchard. The intensive rate increased net primary productivity (Mg C ha-1) and significantly increased N productivity (kg N ha-1) and net N mineralization (mg N kg-1 soil d-1). Use of 15N tracers demonstrated short and long-term mechanisms of soil N retention. These low organic matter soils (0.3-0.5%) rapidly immobilized fertilizer nitrate within 36 h of N application and 15N in tree biomass recycled back into soil organic matter over five years. Both fertilizer rates resulted in high crop and total N recovery efficiencies of 90% and 98% for the reduced rate, and 72% and 80% for the intensive rate. However, there was no difference in the proportion of N losses to N inputs due to a significant gain in soil total N (TN) in the intensive rate. Higher soil TN significantly increased net N mineralization and a larger gain in soil organic carbon (SOC) from the intensive rate offset nitrous oxide (N2O) emissions, leading to significantly lower net GWP of -1.64 Mg CO2-eq ha-1 yr-1 compared to -1.22 Mg CO2-eq ha-1 yr-1 for the reduced rate. Our study demonstrates increased N cycling and climate mitigation from intensive fertilizer N use in this orchard agroecosystem, implying a fundamentally different result than seen in conventional annual cropping systems.

Grower Analysis of Organic Matter Amendments in California Orchards.

Organic matter amendments supply crop nutrients and enhance soil health, yet information specific to orchards is lacking. A survey was conducted to analyze use of these materials by California almond [ (Mill.) D.A. Webb] growers. Significant differences were observed for benefits, concerns, and accessibility to manure and green waste sources and between users and nonusers. Use patterns were significantly influenced by heavy and light users, farm size, and geographic region. Enhanced soil biology was the main benefit attributed to organic matter amendments by both users and nonusers. Nonusers showed greater concern for food safety compared to users, and all growers reported greater concern for food safety from manure. The greatest adoption of organic matter amendments occurred on small farms (≤170 ha) located in the north San Joaquin Valley in California. Greater accessibility to manure correlated with presence of dairy farms. Poorer accessibility ratings by nonusers suggest access is a barrier to adoption, as opposed to nonusers having an undesirable view of the practice. Common management included applying organic matter amendments during tree dormancy from manure sources in composted forms with no-till. Heavy users on small farms exhibited the greatest year-to-year consistency and were more flexible with selection of sources and diverse in application methods. Large farms (>170 ha) were less likely to use organic matter amendments every year and less likely to apply them on all their farm area. This study identifies a number of strategies to fill knowledge gaps, increase practice awareness, and overcome barriers to adoption.