Agriculture

Time Series: Basic Analysis

Background

This post is the first in a series of upcoming blog that tries to describe application of a lesser used technique in econometrics – time series analysis. I make extensive use of datasets available in several R packages – mostly the tsibbledata package. Furthermore, an external package hosted in github.com/FinYang/tsdl repo will be used.

Correlation and pathway analysis with path diagrams

Background

Correlation study is one of the most extensively yet not fully appreciated topic. It forms the backbone of several other inferential studies. Path analysis, on a similar note, is a derived technique that explains directed dependencies among a set of variables. It is almost exactly a century old now and still finds uses in several fields of causal inference.

In oder to understand the process of causal inference (thought to be successor of path analysis), it is important to understand the basics about categories of variables. Below I have pointed out some of the concepts.

World production and grain composition of major cultivated species

Context

Out of crops raised for their seed/grains (listed under 35 species, by FAO; FAOSTAT, 2014), only 22 species are produced in substantial amounts. Species of graminae and leguminosae families alone account for about 85 percent of the total grain production. As presented here in Table 1.

Production

(\#tab:world-production)Global production of major cultivated crops
Crop Crop species World production^[Average of 2011 to 2014, FAOSTAT (2016)] (1000 t)
Poaceae
Maize Zea mays L. 950394
Rice Oryza sativa L. 733424
Wheat Triticum spp. 700828
Barley Hordeum vulgare L. 138252
Sorghum Sorghum bicolor (L.) Moench 58647
Millet^[May include members of other genera such as Pennisetum, Papspalm, Setoria and Echinochla] Panicum miliaceum L. 26528
Oat Avena sativa L. 22639
Rye Secale cereale L. 14906
Triticale X Triticosecale Wittm ex A. Camus 14653
Fabaceae
Soybean Glycine max (L.) Merrill 272426
Groundnut^[In the shell] Arachis hypogaea L. 41366
Bean^[Also includes other species of Phaseolus and, in some countries, Vigna species.] Phaseolus vulgaris L. 23898
Chickpea Cicer arietinum L. 12735
Pea, dry^[May include P. arvense (field pea).] Pisum sativum L. 11013
Cowpea Vigna unguiculata (L.) Walp. 6661
Lentil Lens culinaris Medikus 4831
Broad bean Vicia faba L. 4332
Pigeon pea Cajanus cajan L. Millsp. 4454
Others^[Rapeseed is in the Brassicaceae, sunflower and safflower are in the Asteraceae, and sesame is in Pedaliaceae.]
Rapeseed^[May include industrial and edible (canola) types, data from some countries includes mustard (Brassica juncea (L.) Czern, et Coss)] Brassica napus L., B campestris L. 67789
Sunflower Helianthus annuus L. 40931
Sesame Sesamum indicum L. 4738
Safflower Carthamus tinctoris L. 776

Grain composition

(\#tab:grain-comp)Global production of major cultivated crops
Crop Crop species Harvested unit Seed carbohydrate (g_per_kg) Seed oil (g_per_kg) Seed protein (g_per_kg)
Poaceae
Maize Zea mays L. Caryopsis 800 50 100
Rice Oryza sativa L. Caryopsis 880 20 80
Wheat Triticum spp. Caryopsis 750 20 120
Barley Hordeum vulgare L. Caryopsis^[Harvested grain usually includes the lemma and palea] 760 30 120
Sorghum Sorghum bicolor (L.) Moench Caryopsis 820 40 120
Millet^[May include members of other genera such as Pennisetum, Papspalm, Setoria and Echinochla] Panicum miliaceum L. Caryopsis 690 50 110
Oat Avena sativa L. Caryopsis^[Harvested grain usually includes the lemma and palea] 660 80 130
Rye Secale cereale L. Caryopsis 760 20 120
Triticale X Triticosecale Wittm ex A. Camus Caryopsis 594 18 131
Fabaceae
Soybean Glycine max (L.) Merrill Non-endospermic seed 260 170 370
Groundnut^[In the shell] Arachis hypogaea L. Non-endospermic seed 120 480 310
Bean^[Also includes other species of Phaseolus and, in some countries, Vigna species.] Phaseolus vulgaris L. Non-endospermic seed 620 20 240
Chickpea Cicer arietinum L. Non-endospermic seed 680 50 230
Pea, dry^[May include P. arvense (field pea).] Pisum sativum L. Non-endospermic seed 520 60 250
Cowpea Vigna unguiculata (L.) Walp. Non-endospermic seed 570 10 250
Lentil Lens culinaris Medikus Non-endospermic seed 670 10 280
Broad bean Vicia faba L. Non-endospermic seed 560 10 230
Pigeon pea Cajanus cajan L. Millsp. Non-endospermic seed 560 20 250
Others^[Rapeseed is in the Brassicaceae, sunflower and safflower are in the Asteraceae, and sesame is in Pedaliaceae.]
Rapeseed^[May include industrial and edible (canola) types, data from some countries includes mustard (Brassica juncea (L.) Czern, et Coss)] Brassica napus L., B campestris L. Non-endospermic seed 190 480 210
Sunflower Helianthus annuus L. Cypsela 480 290 200
Sesame Sesamum indicum L. Non-endospermic seed 190 540 200
Safflower Carthamus tinctoris L. Cypsela 500 330 140

References

Page 3 and 4, Seed Biology and Yield of Grain Crops, 2nd Edition

Seed growth rate of major cultivated crops

Context

(\#tab:smr-vegetables)Seed growth rate of common cultivated crop species
Crop Number of cultivars Seed growth rate_mean (mg per seed per day) Seed growth rate_range (mg per seed per day) Effective filling period_mean (day) Effective filling period_range (day) Maximum size_mean (mg per seed) Maximum size_range (mg per seed)
Cereals:
Wheat (Triticum aestivum L.) 26 1.4 2.1-1.0 29 45-19 41 55-23
Barley (Horedum vulgare L.) 13 1.6 2.4-0.6 25 43-18 38 50-22
Rice (Oryza sativa L.) 12 1.2 2.0-0.9 24 36-12 28 50-20
Sorghum (Sorghum bicolor (L.) Moench.) 9 0.9 1.9-0.4 23 42-20 28 37-19
Maize (Zea mays L.) (inbreds) 22 7.4 9.7-3.6 31 39-23 228 322-86
Maize (Zea mays L.) (hybrids) 10 8.8 10.4-7.0 35 41-23 302 410-229
Legumes:
Soybean (Glycine max (L.) Merr.) 21 6.8 14.7-3.6 29 46-13 202 484-84
Bean (Phaseolus vulgaris L.) 20 18.9 33.1-10.2 18 24-14 345 540-190
Pea (Pisum sativum L.) 5 10.5 14.3-5.6 22 35-12 195 224-150
Field pea (P. arvense) 2 9.5 13.0-6.0 25 32-18 211 232-190
Broad bean (Vicia faba L.) 3 36.9 55.0-20.0 31 57-16 1104 2017-414
Cowpea (Vigna unguiculata L. Walp) 3 8.4 12.2-4.4 8 9.0-7.0 73 122-32
Groundnut (Arachis hypogaea) 2 12.8 14.0-11.6 44 45-43 563 626-500
Oil seeds:
Flax (Linum usitatissimum L.) 2 0.2 0.3-0.2 31 35-27 8 8.0-7.0
Sunflower (Helianthus annuus L.) 7 1.6 2.0-1.2 34 48-30 54 75-39

References

Page 46, Seed Biology and Yield of Grain Crops, 2nd Edition

Maize seed value chain

Introduction

There are a great deal of influencers along the chain of generating finished seed material from a produce offered by farmers. As we discussed earlier in this post, the process takes on a fairly complex pathway while certification and processing requirements are being met. Carrying out the process requires resources, a large amount of them. This post tries to quantitatively explain a general expense scheme of a typical company in Maize seed business (Note, however, that the concepts generalize well to both Hybrid and OP seeds). So why approach to business management through the cost concept? I’ll just bullet point some of the direct benefits of thinking quantitatively.

Seed multiplication ratio and its significance in production planning

Context

SMR of Cereal crops

SMR of vegetable crops

(\#tab:smr-vegetables)Seed multiplication ratio of common vegetable crops
SN Crop Seed rate (per ha) Seed yield Seed Multiplication Ratio
1 Broad leaf mustard 600 600 1000.0
2 Bottle gourd 5000 160 32.0
3 Bitter gourd 5000 120 24.0
4 Broccoli 600 600 1000.0
5 Carrot 5000 600 120.0
6 Cabbage (Drum head) 600 800 1333.3
7 Cabbage (Golden acre) 600 700 1166.7
8 Cauliflower (Kathmandu local) 500 300 600.0
9 Cauliflower (Snowball) 500 240 480.0
10 Stringy beans 20000 600 30.0
11 Hot pepper 1000 160 160.0
12 Chenopodium 10000 700 70.0
13 Cucumber 3000 100 33.3
14 Pole bean 30000 800 26.7
15 Bush bean 80000 800 10.0
16 Knolkhol 1000 800 800.0
17 Onion 10000 500 50.0
18 Pea 100000 1000 10.0
19 Pumpkin 5000 160 32.0
20 Radish (White neck) 6000 800 133.3
21 Radish (Minnow early) 6000 500 83.3
22 Capsicum 1000 100 100.0
23 Squash 10000 200 20.0
24 Sponge gourd 5000 200 40.0
25 Swisschard 20000 800 40.0
26 Spinach 15000 500 33.3
27 Tomato 500 100 200.0
28 Turnip 5000 800 160.0
29 Watermelon 4000 100 25.0
30 Onion (For western mid hills) 10000 800 80.0

References

Adapted from Nepali writing of document on Seed production technology of major vegetable crops cultivated in Nepal (Basanta Chalise and Dr. Tul Bahadur Pun). Authors cite: FAO, 1984 for their tabulation.

Seed Business: Process Management Guide

The problem

Seed business is a multifaceted undertake. Although most businesses suffer strong feedback effects, seed business are more markedly left with those than most others. Let us refer to a simple case description to recapitulate just how pronounced it can be, and we are talking about the success in a long term venture.

Bean: Description of Agromorphology

Introduction

Agromorphology constitutes what’s observable and that which is economic. Because agriculture has important connection to economy, this connection is at best rung everywhere though what agriculture reveres and, and when talked modestly, relies on: Crops.

Unsurprisingly, finer details that agriculture touches upon to make ends met (processes and resources involved along the Production-Consumption chain) are convoluted. We just cannot discourse enough. This post, however, tries to make a connection between the economy and botany, however through generalization and prioritization.

Rice landraces of Nepal: A reflection of past

Background

This is really a brash article meant to convey exactly what title hints at. Much of the contents are translated/transliterated from “Rice Science and Technology in Nepal: A historical, socio-cultural and technical compendium”, which was published in 2017. As of now, primary authors are left uncredited, but as entries become widely accepted, original writers and publishers will be duely recognized.

Design and analysis of spit plot experiments

Split plot design

Design and fieldbook template

In a field experiment to test for effects of fungicide on crop, treatment of fungicides may be distinguised into multiple factors – based on chemical constituent, based on formulation, based on the mode of spray, etc. In a general case scenario where two former factors could be controlled, factor combinations may be organized in several different ways. When fully crossed implementation is not possible, split plot design comes to the rescue.

Layout and visualization of experimental design

Functional approach to creating and combing multiple plots

This approach highlights features of gridExtra package that allows combining multiple grob plots using function calls. We explicitly use lapply/split or similar class of purrr functions to really scale the graphics.

We load a Hybrid maize trial dataset, with fieldbook generated using agricolae::design.rcbd(). The dataset looks as shown in Table 1, after type conversion and cleaning.

(\#tab:rcbd-maize-fieldbook)Intermediate maturing hybrids with 50 entries each in 3 replicated blocks
Rep Block Plot Entry col row tillering moisture1 moisture2 Ear count Plant height
1 1 1 1 1 1 3.0 3.5 35 270
1 1 2 3 1 2 3.0 3.5 25 266
1 1 3 18 1 3 3.5 4.0 30 261
1 1 4 32 1 4 4.0 4.5 26 224
1 1 5 37 1 5 4.0 4.5 30 268
1 2 6 27 1 6 4.0 4.5 20 268
1 2 7 21 1 7 4.0 4.5 25 277
1 2 8 13 1 8 3.5 4.0 25 264

For the given dataset, we can draw on the information that Rep variable was used as field level blocking factor (Although separate, Block, variable exists, it was nested inside the Rep.) Therefore, to begin with, we ignore other spatial grouping variable. Now, since the grid graphics only requires two way represenation of plotting data, we have row and col information feeding for that.

Varietal database of lentil in Nepal

This one is my effort to compose an updated database on the current situation of lentil germplasm in Nepal. I’ve managed to list out the varieties that have been made available so far (either through release or registration process). Although, a lot of other popular genotypes are trending in cultivation as of now. I consider NARC’s varietal catalogs to be the most authentic, so have borrowed most of the information from these published documents.

Flowering phenology of Rice genotypes in irrigated tropical condition of Nepal

Dhangadhi of Kailali, Nepal remains relatively hotter during summer season with respect to average condition of terai agro-ecology. A regime of high day temperature with bright sunshine and cool night temperature along with plenty of seasonal rain during larger part of rice growing season favors good growth of rice crop in the region.

Based on a range of cultivation sowings carried out in the summer/rainy season of 2018, flowering dates were recorded for each plot of varieties, and the duration since planting to flowering was determined. Here, flowering time is ascribed to the period when approximately 50% of the anthers in each plots could be promptly seen as extruding. I summarize the resulting days to flowering period in the bar chart shown in Figure 1.

Resource optimization

library(lpSolve)
library(tidyverse)

Issue

A farmer has 600 katthas of land under his authority. Each of his katthas of land will either be sown with Rice or with Maize during the current season. Each kattha planted with Maize will yield Rs 1000, requires 2 workers and 20 kg of fertilizer. Each kattha planted with Rice will yield Rs 2000, requires 4 workers and 25 kg of fertilizers. There are currently 1200 workers and 11000 kg of fertilizer available.