Analisis Faktor yang Mempengaruhi Pendapatan USAhatani Sayuran di Kecamatan Sungai Gelam Kabupaten Muaro Jambi

Penelitian ini bertujuan untuk melihat besarnya pendapatan USAhatani sayuran di Kecamatan Sungai Gelam Kabupaten Muaro Jambi. Pemilihan lokasi penelitian dilakukan dengan sengaja atas dasar pertimbangan bahwa di Kecamatan Sungai Gelam merupakan salah satu daerah yang mengusahakan sayuran terbesar di Kabupaten Muaro Jambi.Sampel dalam penelitian ini adalah petani sayuran di Kecamatan Sungai Gelam Kabupaten Muaro Jambi.Penelitian dilakukan dari tanggal 10 September 2014 sampai dengan tanggal 10 Oktober 2014 dengan menggunakan metode simple random sampling. Hasil penelitian ini menunjukkan bahwa Rata – rata pendapatan USAhatani sayuran petani responden di daerah penelitian yaitu Rp. 21.673.293,87 /Tahun dengan rata – rata luas lahan sebesar 0,26 ha. Data ini menunjukkan bahwa kegiatan USAhatani sayuran yang dilakukan petani di Kecamatan Sungai Gelam Masih berskala kecil.Pendapatan USAhatani sayuran di daerah penelitian secara nyata dipengaruhi oleh variabel luas lahan dan modal dengan nilai koefisien positif.Hal ini berarti semakin tinggi luas lahan dan modal yang digunakan, maka pendapatan USAhatani sayuran tinggi.Sedangkan tenaga kerja tidak memberikan pengaruh secara nyata terhadap pendapatan USAhatani sayuran

Pearson correlations inference quality deteriorates with decreasing diversity.

<p>Basis data was simulated with a known correlation structure. OTU counts were generated by randomly drawing from the basis, and were subsequently subject to both correlation inference procedures. (A–C) True basis correlation network. (D–F) Networks inferred using standard procedure. (G–I) Networks inferred using SparCC. The average community diversities, as given by the Shannon entropy effective number of components , used in the simulations and observed in the HMP data are indicated on left indicates. As in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002687#pcbi-1002687-g001" target="_blank">Fig. 1</a>, nodes represent OTUs, with size reflecting the OTU's average fraction in the community. Nodes represent OTUs, with size reflecting the OTU's average fraction in the community. Edges between nodes represent correlations between the nodes they connect, with edge width and shade indicating the correlation magnitude, and green and red colors indicating positive and negative correlations, respectively. For clarity, only edges corresponding to correlations whose magnitude is greater than 0.3 are drawn.</p

Similar correlation networks are observed for real world vs. randomly shuffled bacterial abundance data.

<p>Correlation networks based on 16S rRNA gene survey data collected as part of the Human Microbiome Project (HMP), inferred using Pearson correlations (left column), and SparCC (right column). Additionally, Pearson correlation networks were inferred from shuffled HMP data (middle column), where all OTUs are independent. The Pearson networks inferred from shuffled data show patterns similar to the ones seen in the Pearson networks of the real data, especially for low diversity body sites. This indicates that the observed Pearson network structure may be due to biases inherent in compositional data rather than a real biological signal. In contrast, no significant correlation were inferred from the shuffled data using SparCC (data not shown). Nodes represent OTUs, with size reflecting the OTU's average fraction in the community. Edges between nodes represent correlations between the nodes they connect, with edge width and shade indicating the correlation magnitude, and green and red colors indicating positive and negative correlations, respectively. For clarity, only edges corresponding to correlations whose magnitude is greater than 0.3 are drawn. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002687#pcbi.1002687.s002" target="_blank">Fig. S1</a> for all 18 HMP body sites.</p

SparCC outperforms standard inference.

<p>Root-mean-square error (RMSE) of both Pearson (A) and SparCC (B) inferred correlations, as a function of the density of the underlying correlation network, as given by the probability that any pair of components be strongly correlated , and community diversity, as given by the Shannon entropy effective number of components . SparCC errors are smaller than Pearson errors for all parameter values. For the maximal diversity plotted, 50 effective OTU, the inference error obtained using Pearson correlations is greatly decreased. Therefore, it is likely that Pearson correlations perform well on gene expression data, where the effective number of genes is typically in the hundreds or thousands. For each combination of density and diversity, multiple basis correlation networks were randomly generated, and corresponding data was sampled and used for correlation estimation. Dots labeled mid-vagina and gut indicate the average diversity observed in the mid-vagina and gut communities, and the density of their estimated correlation networks. Dots labeled 2D–I indicate the diversity and density used to generate the communities analyzed in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002687#pcbi-1002687-g002" target="_blank">Fig. 2</a>.</p

Flow chart of iterative basis correlation inference procedure.

<p>Flow chart of iterative basis correlation inference procedure.</p

HMP correlation networks inferred using SparCC.

<p>Networks inferred using SparCC from the same data as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002687#pcbi-1002687-g001" target="_blank">Fig. 1</a> (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002687#pcbi.1002687.s003" target="_blank">Fig. S2</a> for SparCC networks of all HMP body sites). No correlations with magnitude greater than the 0.3 cutoff were inferred from the shuffled data (not shown). Nodes represent OTUs, with size reflecting the OTU's average fraction in the community, and color corresponding to the phylum to which the OTU belongs. Edges between nodes represent correlations between the nodes they connect, with edge width and shade indicating the correlation magnitude, and green and red colors indicating positive and negative correlations, respectively. For clarity, only edges corresponding to correlations whose magnitude is greater than 0.3 are drawn, and unconnected nodes are omitted. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002687#pcbi.1002687.s007" target="_blank">Fig. S6</a> for all 18 HMP body sites.</p

False positive rates are reduced by batch-correction methods.

<p>Random sets of 40 Baxter controls and random sets of 40 Zeller controls were selected for null case-control comparisons (20 iterations). Smaller points show the fraction of p-values ≤ 0.05 within a given iteration, while larger dots show the average value across all 20 iterations. Within each category, smaller points are randomly jittered along the x-axis for better visualization. The fraction of p-values ≤ 0.05 is highly inflated for non-normalized data (red dashed line shows the null-expectation for p-values). Only abundant OTUs (detected in at least a third of case or control samples) were included in this analysis.</p

Batch effects between healthy controls from different studies can be reduced by ComBat and percentile-normalization.

<p>Non-metric multidimensional scaling (NMDS) plot showing the distribution of healthy controls from three colorectal cancer studies in ordination space (Bray-Curtis distances of relative abundance OTU-level data). Despite standardized bioinformatic processing, healthy patients differed significantly in their gut microbiomes across studies (PERMANOVA p < 0.001; batch accounts for 6.342% of the total variance). Studies were still significantly different after applying ComBat, an established batch-correction method (PERMANOVA p < 0.01). However, percentile-normalization did a better job of stabilizing the variance across studies and removed any apparent batch effect (PERMANOVA p > 0.5).</p

Benchmarks for the speed of the distribution criteria.

<p>Benchmarks for the speed of the distribution criteria.</p

Comparisons of communities analyzed by different methods.

<p>dbOTU3 produces nearly identical results with dbOTU2 when visualized in a principal coordinate analysis ordination plot. Each point represents a community resulting from analysis of the mock community data one of the OTU callers. (The two triangles representing dbOTU2 and dbOTU3 always appear on top of one another, making a six-pointed triangle.) The “true composition” is the community composition expected based on how the communities were constructed. The principal components were computed using a matrix of the square roots of the Jensen-Shannon divergence between each pair of computed community compositions.</p