Moving Past the Routine: Precision Management for Alfalfa and Hay Crops

Soil testing is made up of four distinct activities, collecting the soil sample, analyzing the sample, interpreting the results, and providing fertilizer recommendations that account for the fertilizer source, timing of application, rate of application, and placement of the fertilizer. Traditionally, collecting a soil sample was viewed as the limiting step because a recommendation is only as good as the sample that it is based on. With traditional soil sampling, we attempt to represent the field’s average nutrient status. Typically, you would want one sample for every 10 – 20 acres. A sample should be collected to the depth prescribed by the lab (4” for untilled fields). If areas within a field are very different due to previous management or natural features, such as topography or soil texture, split the field up and collect samples from each distinct area. Each sample sent to the lab should be a composite of 12 or more soil cores

Rcf1 and Rcf2, Members of the Hypoxia-induced Gene 1 Protein Family, Are Critical Components of the Mitochondrial Cytochrome bc1-cytochrome Oxidase Supercomplex

We report that Rcf1 (formerly Aim31), a member of the conserved hypoxia-induced gene 1 (Hig1) protein family, represents a novel component of the yeast cytochrome bc1-cytochrome c oxidase (COX) supercomplex. Rcf1 (respiratory supercomplex factor 1) partitions with the COX complex, and evidence that it may act as a bridge to the cytochrome bc1 complex is presented. Rcf1 interacts with the Cox3 subunit and can do so prior to their assembly into the COX complex. A close proximity of Rcf1 and members of the ADP/ATP carrier (AAC) family was also established. Rcf1 displays overlapping function with another Hig1-related protein, Rcf2 (formerly Aim38), and their joint presence is required for optimal COX enzyme activity and the correct assembly of the cytochrome bc1-COX supercomplex. Rcf1 and Rcf2 can independently associate with the cytochrome bc1-COX supercomplex, indicating that at least two forms of this supercomplex exist within mitochondria. We provide evidence that the association with the cytochrome bc1-COX supercomplex and regulation of the COX complex are a conserved feature of Hig1 family members. Based on our findings, we propose a model where the Hig1 proteins regulate the COX enzyme activity through Cox3 and associated Cox12 protein, in a manner that may be influenced by the neighboring AAC proteins

Measurements of OH Reactivity Using a Chemical Ionization Mass Spectrometry Technique

Typically tropospheric chemical models overestimate [OH] when compared to measurements. This discrepancy is usually attributed to incompletely modeling OH sinks due to a lack of measurements. The determination of OH reactivity provides an independent means to quantify the overall contribution of these unmeasured trace species to OH oxidation. Therefore, a new instrument was developed to measure the total loss rate of OH to chemical species in ambient air using the CIMS technique. Measurements were performed as part of two field campaigns, the Nucleation in Forests (NIFTy) study in Indiana during May 2008 and the Ocean-Atmosphere-Sea Ice- Snowpack (OASIS) Spring 2009 intensive near Barrow, Alaska. The NIFTy study provided an opportunity to intercompare with a second OH reactivity system using the laser induced fluorescence detection technique as well as measure in an environment that has published results to compare to. The OASIS study provided an opportunity to measure in a previously unmeasured environment with a suite of complimentary measurements to allow for comparison between measured and calculated OH reactivity. The results of the NIFTy study showed good agreement between the two instruments as well as previous published results for forest and mixed use environments. OASIS provided the first ground based measurements of OH reactivity in the Arctic and the lowest average for measured OH reactivity of published results, about 2.5 s-1. OH reactivity was found to be lower during ozone depletion events than non-ozone depletion events, but not significantly

Trapping Phosphorus in Runoff with a Phosphorus Removal Structure

Reduction of phosphorus (P) inputs to surface waters may decrease eutrophication. Some researchers have proposed fi ltering dissolved P in runoff with P-sorptive byproducts in structures placed in hydrologically active areas with high soil P concentrations. Th e objectives of this study were to construct and monitor a P removal structure in a suburban watershed and test the ability of empirically developed fl ow-through equations to predict structure performance. Steel slag was used as the P sorption material in the P removal structure. Water samples were collected before and after the structure using automatic samples and analyzed for total dissolved P. During the fi rst 5 mo of structure operation, 25% of all dissolved P was removed from rainfall and irrigation events. Phosphorus was removed more effi ciently during low fl ow rate irrigation events with a high retention time than during high fl ow rate rainfall events with a low retention time. Th e six largest fl ow events occurred during storm fl ow and accounted for 75% of the P entering the structure and 54% of the P removed by the structure. Flow-through equations developed for predicting structure performance produced reasonable estimates of structure “lifetime” (16.8 mo). However, the equations overpredicted cumulative P removal. Th is was likely due to diff erences in pH, total Ca and Fe, and alkalinity between the slag used in the structure and the slag used for model development. Th is suggests the need for an overall model that can predict structure performance based on individual material properties

Trapping Phosphorus in Runoff with a Phosphorus Removal Structure

Reduction of phosphorus (P) inputs to surface waters may decrease eutrophication. Some researchers have proposed fi ltering dissolved P in runoff with P-sorptive byproducts in structures placed in hydrologically active areas with high soil P concentrations. Th e objectives of this study were to construct and monitor a P removal structure in a suburban watershed and test the ability of empirically developed fl ow-through equations to predict structure performance. Steel slag was used as the P sorption material in the P removal structure. Water samples were collected before and after the structure using automatic samples and analyzed for total dissolved P. During the fi rst 5 mo of structure operation, 25% of all dissolved P was removed from rainfall and irrigation events. Phosphorus was removed more effi ciently during low fl ow rate irrigation events with a high retention time than during high fl ow rate rainfall events with a low retention time. Th e six largest fl ow events occurred during storm fl ow and accounted for 75% of the P entering the structure and 54% of the P removed by the structure. Flow-through equations developed for predicting structure performance produced reasonable estimates of structure “lifetime” (16.8 mo). However, the equations overpredicted cumulative P removal. Th is was likely due to diff erences in pH, total Ca and Fe, and alkalinity between the slag used in the structure and the slag used for model development. Th is suggests the need for an overall model that can predict structure performance based on individual material properties

The Hermeneutics of Artificial Intelligence

The papers in the following volume are the outcome of a three-year long interdisciplinary research project. The project began with an in-person meeting hosted and funded by the Daimler und Benz Stiftung in Germany in March 2020 (the world was shutting down one nation at a time as we met). During the pandemic we continued to meet monthly online with support from Memorial University of Newfoundland. From the beginning it was the goal of the Working Group on Intelligence (WGI), as we called ourselves, to broaden and deepen the AI debate with a more nuanced understanding of intelligence than is common in cognitive and computer science discussions of AI. We wished to draw on the history of philosophy, ecology, and the philosophy of mind to establish that intelligence is meant in many senses, to use an Aristotelian expression. The clarification of these various meanings is essential to the discussion around the ethics of AI, especially the question concerning the possibility of strong AI or Artificial General Intelligence