Soil property differences and irrigated-cotton lint yield— Cause and effect? An on-farm case study across three cotton-growing regions in Australia

The average lint yield of irrigated cotton in Australia ranges from 2270 to 3700kg/ ha, but yields vary substantially between farms and also between fields on the same farm. Differences in soil properties may cause these yield variations. Identifying which factors are causal and what management can be implemented to mitigate the impacts should help optimize inputs and improve profits. During the 2018–2019 summer cotton-growing season, a paired-field comparison approach was used to investigate and improve the understanding of soil property induced irrigated cotton yield differences within five farms across three regions of NSW, Australia. The paired fields at each farm recorded an average lint yield difference of >284kg/ha (measured in 2018–2019 or 5-year average lint yield). Several soil properties differed between the paired fields at each farm comparison. The soil organic carbon stocks were higher in the higher-yielding fields at all the farm comparisons and the normalized lint yield percentage was positively correlated with soil organic carbon stocks. Soil sodicity was higher in the lower yielding fields at 3 of the 5 comparisons. Results for most soil nutrient tests were above the recommended critical concentrations for Australian cotton production. A stepwise linear regression excluding soil nutrients that were above soil test critical values for crop response and below crop toxicity levels indicated the lint yield was positively correlated with SOC stocks and negatively correlated with sodicity and bulk density. No earthworms were detected during visual soil assessment or soil sampling across all the sites. Visual soil assessment was not a sensitive predictor of cotton crop performance. Comparing soil properties using a paired field approach may assist cotton growers in understanding the factors behind yield differences. A similar strip comparison approach could be adopted for within-field variability by dividing the fields into discrete performance zones and assessing the soil properties of each zone separately.284kg/ha (measured in 2018–2019 or 5-year average lint yield). Several soil properties differed between the paired fields at each farm comparison. The soil organic carbon stocks were higher in the higher-yielding fields at all the farm comparisons and the normalized lint yield percentage was positively correlated with soil organic carbon stocks. Soil sodicity was higher in the lower yielding fields at 3 of the 5 comparisons. Results for most soil nutrient tests were above the recommended critical concentrations for Australian cotton production. A stepwise linear regression excluding soil nutrients that were above soil test critical values for crop response and below crop toxicity levels indicated the lint yield was positively correlated with SOC stocks and negatively correlated with sodicity and bulk density. No earthworms were detected during visual soil assessment or soil sampling across all the sites. Visual soil assessment was not a sensitive predictor of cotton crop performance. Comparing soil properties using a paired field approach may assist cotton growers in understanding the factors behind yield differences. A similar strip comparison approach could be adopted for within-field variability by dividing the fields into discrete performance zones and assessing the soil properties of each zone separately

In-Datacenter Performance Analysis of a Tensor Processing Unit

Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit (TPU)---deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs (caches, out-of-order execution, multithreading, multiprocessing, prefetching, ...) that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X - 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X - 80X higher. Moreover, using the GPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.Comment: 17 pages, 11 figures, 8 tables. To appear at the 44th International Symposium on Computer Architecture (ISCA), Toronto, Canada, June 24-28, 201

Cotton strip assay detects soil microbial degradation differences among crop rotation and tillage experiments on Vertisols

The cotton strip assay (CSA) is a simple and inexpensive method of evaluating management effects on soil mi-crobial decomposition. The average loss of tensile strength of cotton strips buried 3 to 35 days in soils from two long-term tillage and crop-rotation experiments was of the order: cotton-wheat rotation > minimum-tillage cotton monoculture > maximum-tillage cotton monoculture. The study suggests CSA can be an effective indi-cator to delineate microbial activity, soil organic carbon or crop biomass as influenced by agricultural practices in cotton fields. minimum-tillage cotton monoculture > maximum-tillage cotton monoculture. The study suggests CSA can be an effective indi-cator to delineate microbial activity, soil organic carbon or crop biomass as influenced by agricultural practices in cotton fields. maximum-tillage cotton monoculture. The study suggests CSA can be an effective indi-cator to delineate microbial activity, soil organic carbon or crop biomass as influenced by agricultural practices in cotton fields

Genotypic variation for lodging tolerance in spring wheat: wider and deeper root plates, a feature of low lodging, high yielding germplasm

Plant lodging reduces yield and quality of irrigated and rainfed spring wheats alike. Local and imported germplasm was screened to identify consistently higher-yielding genotypes with low plant lodging for the north-eastern Australian wheat belt. Using field level treatments, such as fertilisation and tactical overhead irrigation to consistently simulate scenarios leading to lodging in the target region, high reproducibility of lodging rankings was achieved in multi-environment experiments. In separate experiments in two years, detailed phenotyping of selected genotypes in field plots was implemented for traits underpinning stem and root type lodging. Multi-environment and phenotyping experiments ranked genotypes similarly in terms of lodging score. In the phenotyping experiments, root plate spread from field grown plants consistently emerged as a trait able to discriminate low lodging, high yielding germplasm from a multi-trait analysis quantifying genotypic correlations. If the root plate spread was greater than or equal to 5.5 cm, the lodging scores were small, and yield was high. Importantly, root plate spread phenotyped on plants growing at uniform planting density was found to be highly heritable (above 0.80), with a high genotypic correlation (0.80) across environments and strong association with structural rooting depth. A simplified phenotyping approach is discussed based on the main traits driving lodging tolerance and others routinely measured in breeding programs