for crop nutrient removal are an important component of nutrient management planning and crop production. Effective nutrient management requires an accurate accounting of Although state agronomy guides and other sources nutrients removed from soils in the harvested portion of a crop. Because the typical crop nutrient values that have historically been used often publish values for crop nutrient removal, the origimay be different under current production practices, a study was nal studies on which those values are based are seldom conducted to measure nutrient uptake in grain harvested in 1998 and cited. Also, the values that were established in the past 1999 from 23 site-years in the Mid-Atlantic region of the USA. There may not be correct for current agronomic technologies were 10 hybrids included in the study, but each site grew only one such as hybrid, higher plant population, yield potential, hybrid each year. Corn (Zea mays L.) production practices followed fertilizer practice, and soil conditions. Furthermore, local state extension recommendations. Minimum, maximum, and there is a need to re-evaluate crop nutrient removal mean corn grain yields were 4.9, 16.7, and 10.3 Mg ha Ϫ1. Nutrient values for corn as several states in the Mid-Atlantic concentrations were determined on grain samples oven-dried at 70؇C USA now mandate the development of comprehensive for 24 h. Minimum, maximum, and median nutrient concentration nutrient management plans (Simpson,

The consensus of soil fertility specialists working in the northeast USA was that soil testing and recommendation systems for P needed to be reexamined because of recent changes in soil testing methodology in the laboratory and corn (Zea mays L.) production technology in the field. Soil tests (M-COL, MM-COL, B-ICP, M1-ICP, and M3-ICP) were performed by either colorimetry or inductively coupled plasma (ICP) emission spectroscopy on samples from soil test calibration studies conducted during 1998 to 1999 at 51 experimental sites chosen to represent a range of soils, including Ultisols, Spodosols, and Alfisols, in northeastern states (Connecticut, Delaware, Massachusetts, Maryland, Maine, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, and West Virginia). The mean P measured by M-COL, MM-COL, B-ICP, M1-ICP, and M3-ICP was 8.3, 6.6, 148, 66, and 121 mg P kg 21 , respectively. Production practices followed local state extension recommendations at each site and included P fertilizer treatments: none, 15 kg P ha 21 banded, or 60 kg P ha 21 broadcast. Combined analysis of variance over sites showed that plant height at 35 d after planting, silk emergence, grain yield, and grain dry down were enhanced by the broadcast P treatment. There were yield increases (P , 0.10) to the band treatment at only four sites and to the broadcast treatment at nine sites. Cate-Nelson statistical analysis of relative yield in relation to soil test P failed to identify soil test P critical levels for any of the soil test methods. The percentage of experimental sites that had soil test P levels below the currently used critical levels in the region ranged from 14 to 65% of the sites. Results showed that 17 to 47% of those sites testing below the critical level exhibited a yield increase (P , 0.10) to broadcast P. Some of the yield responsive sites had soil test P above currently used critical levels. The calibration data obtained from the present study and the relationships examined between soil test P and relative yield do not necessarily Abbreviations: B-ICP, Bray-P1 with inductively coupled plasma emission spectroscopy determination of extracted P; M-COL, Morgan with colorimetric determination of extracted P; MM-COL, Modified Morgan with colorimetric determination of extracted P; M-ICP, Morgan with inductively coupled plasma emission spectroscopy determination of extracted P; MM-ICP, Modified Morgan with inductively coupled plasma emission spectroscopy determination of extracted P; M1-ICP, Mehlich-1 with inductively coupled plasma emission spectroscopy determination of extracted P; M3-ICP, Mehlich-3 with inductively coupled plasma emission spectroscopy determination of extracted P; PSNT, presidedress soil nitrate test.

Minimising the expected mean squared error is one of the fundamental metrics applied to adaptive waveform design for active sensing. Previously, only cost functions corresponding to a lower bound on the expected mean squared error have been expressed for optimisation. In this paper we express an exact cost function to optimise for minimum mean squared error adaptive waveform design (MMSE-AWD). This is expressed in a general form which can be applied to non-linear systems. Additionally, we provide a general example for how this method of MMSE-AWD can be applied to a system that estimates the state using a particle filter (PF). We make the case that there is a compelling reason to choose to use the PF (as opposed to alternatives such as the Unscented Kalman filter and extended Kalman filter), as our MMSE-AWD implementation can re-use the particles and particle weightings from the PF, simplifying the overall computation. Finally, we provide a numerical example, based on a simplified multiple-input-multiple-output radar system, which demonstrates that our MMSE-AWD method outperforms a simple non-adaptive radar, whose beam-pattern has a uniform angular spread, and also an existing approximate MMSE-AWD method.

This paper addresses the problem of qubit routing in first-generation and other near-term quantum computers. In particular, it is asserted that the qubit routing problem can be formulated as a reinforcement learning (RL) problem, and that this is sufficient, in principle, to discover the optimal qubit routing policy for any given quantum computer architecture. In order to achieve this, it is necessary to alter the conventional RL framework to allow combinatorial action space, and this represents a second contribution of this paper, which is expected to find additional application, beyond the qubit routing problem addressed herein. Numerical results are included demonstrating the advantage of the RL-trained qubit routing policy over using a sorting network.

This paper proposes a method of quantum Monte-Carlo integration that retains the full quadratic quantum advantage, without requiring any arithmetic or the quantum Fourier transform to be performed on the quantum computer. No previous proposal for quantum Monte-Carlo integration has achieved all of these at once. The heart of the proposed method is a Fourier series decomposition of the sum that approximates the expectation in Monte-Carlo integration, with each component then estimated individually using quantum amplitude estimation. The main result is presented as theoretical statement of asymptotic advantage, and numerical results are also included to illustrate the practical benefits of the proposed method. The method presented in this paper is the subject of a patent application [Quantum Computing System and Method: Patent application GB2102902.0 and SE2130060-3].