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 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.
Here are the links to lecture notes that I prepared for tutoring preparation classes of Agricultural Economics. Download by clicking on the links below:
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
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.
(\#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 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.
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.
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.
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.
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.
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.
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.
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.