Acumen:
International Journal of
Multidisciplinary Research
ISSN: 3060-4745
IF(Impact Factor)10.41 / 2024
Volume 2, Issue 4
191
Acumen: International Journal of Multidisciplinary Research
R DASTURLASH TILIDA QISHLOQ XO‘JALIGIDAGI IQTISODIY
JARAYONLARNI MODELLASHTIRISH: BUG‘DOY HOSILDORLIGI
MISOLIDA
Normamatova Yulduz Ravshan qizi
TerDU, 2-bosqich magistranti
Annotatsiya
Ushbu maqolada R dasturlash tilidan foydalanib qishloq
xo‘jaligida bug‘doy hosildorligini modellashtirish bo’yicha amaliy yondashuvlar
taqdim etiladi. Modelni qurish jarayoni avvalo oddiy matematik tenglamalar asosida
bayon etiladi, so‘ng Python, Excel VBA va MATLAB tillarida modellashtiriladi.
Yakunda R tilida model ishlab chiqiladi va boshqa tillar bilan qiyosiy tahlil qilinadi.
Maqolada R dasturlash tilining statistik kuchi, grafik imkoniyatlari va amaliy jihatdan
qulayligi ochib beriladi. Shuningdek, hozirgi zamonaviy agrotexnologiyalar uchun R
tilining yangi imkoniyatlari va innovatsion yechimlari yoritiladi.
Kalit so‘zlar:
R dasturlash tili, iqtisodiy modellashtirish, qishloq xo‘jaligi,
bug‘doy hosildorligi, statistik tahlil, regressiya modeli, agroinformatika, Smart
Farming, Python, MATLAB, Excel VBA.
Bugungi kunda iqtisodiy jarayonlarni raqamli modellashtirish innovatsion
qarorlar qabul qilishda muhim ahamiyat kasb etmoqda. Ayniqsa, qishloq xo‘jaligidagi
resurslar samaradorligi, iqlim o‘zgarishlari va hosil prognozlarini aniqlashda statistik
modellashtirish texnologiyalari zarur bo‘lib bormoqda.
Jarayon tanlovi: Bug‘doy hosildorligini modellashtirish
Foydalaniladigan parametrlar:
•
Yillik yog‘in miqdori (mm)
•
O‘rtacha havo harorati (°C)
•
Mineral o‘g‘it sarfi (kg/ga)
•
Natija: Bug‘doy hosildorligi (ts/ga)
3. Oddiy matematik modellashtirish (regressiya tenglamasi)
Fikr: Hosildorlik (Y)
ni 3 ta omilga bog‘lab oddiy chiziqli model tuzamiz:
Acumen:
International Journal of
Multidisciplinary Research
ISSN: 3060-4745
IF(Impact Factor)10.41 / 2024
Volume 2, Issue 4
192
Acumen: International Journal of Multidisciplinary Research
Y = β
0
+ β
1
X
1
+ β
2
X
2
+ β
3
X
3
+ ε
Misol uchun quyidagi qiymatlar olinadi:
Yil
Yog‘in (X₁)
Harorat (X₂)
O‘g‘it (X₃)
Hosildorlik (Y)
2020
400
18.5
200
28.0
2021
350
19.0
220
30.2
2022
420
17.5
210
29.1
2023
390
18.0
230
31.0
Modellashtirish uchta boshqa platformada ko’ramiz:
1. Excel VBA
•
Ma’lumotlar Excelga kiritiladi.
•
VBA kod orqali regresiya tenglamasi quriladi.
•
Vizual grafiklar cheklangan bo‘lsa-da, oddiy prognozlar chiqariladi.
2. Python (NumPy + scikit-learn)
from sklearn.linear_model import LinearRegression
import numpy as np
X = np.array([[400, 18.5, 200], [350, 19.0, 220], [420, 17.5, 210], [390, 18.0, 230]])
Y = np.array([28.0, 30.2, 29.1, 31.0])
model = LinearRegression().fit(X, Y)
print(model.coef_, model.intercept_)
3. MATLAB
X = [400 18.5 200; 350 19.0 220; 420 17.5 210; 390 18.0 230];
Acumen:
International Journal of
Multidisciplinary Research
ISSN: 3060-4745
IF(Impact Factor)10.41 / 2024
Volume 2, Issue 4
193
Acumen: International Journal of Multidisciplinary Research
Y = [28.0; 30.2; 29.1; 31.0];
b = regress(Y, [ones(4,1) X])
4. R dasturlash tilida modellashtirish
# Ma'lumotlar
data <- data.frame(
Yogin = c(400, 350, 420, 390),
Harorat = c(18.5, 19.0, 17.5, 18.0),
Ogit = c(200, 220, 210, 230),
Hosil = c(28.0, 30.2, 29.1, 31.0)
# Model tuzish
model <- lm(Hosil ~ Yogin + Harorat + Ogit, data = data)
# Natijalarni ko‘rish
summary(model)
# Vizualizatsiya
library(ggplot2)
ggplot(data, aes(x = Yogin, y = Hosil)) +
geom_point() +
Acumen:
International Journal of
Multidisciplinary Research
ISSN: 3060-4745
IF(Impact Factor)10.41 / 2024
Volume 2, Issue 4
194
Acumen: International Journal of Multidisciplinary Research
geom_smooth(method = "lm", se = FALSE) +
theme_minimal()
R tilining afzalliklari (asoslangan tahlil)
Ko‘rsatkich
Excel VBA Python
MATLAB R
Ochiq kod
Y
Y
N
Y
Statistika kutubxonalari
Kam
O‘rta
Yuqori
Juda yuqori
Vizualizatsiya qulayligi
O‘rta
O‘rta
O‘rta
Yuqori
O‘rganish qulayligi
O‘rta
O‘rta
Qiyin
Oson
Ma'lumot tahlili
Cheklangan Yaxshi
Yaxshi
Mukammal
Yangiliklar va istiqbollar
. R dasturlash tili nafaqat akademik balki amaliy
sohalarda ham tobora keng qo‘llanilmoqda. So‘nggi yillarda agroinformatika va aqlli
dehqonchilik (Smart Farming) yo‘nalishlarida R asosida ishlab chiqilgan paketlar
(masalan, agromet, climatrends) orqali hosildorlikka iqlim ta’sirini aniqlashda samarali
yechimlar taklif etmoqda.
Xulosa
. Qishloq xo‘jaligida iqtisodiy jarayonlarni raqamli modellashtirish
nafaqat tahlil uchun, balki siyosiy va iqtisodiy qarorlar qabul qilishda muhim vositaga
aylanmoqda. R dasturlash tili statistik chuqurlik, grafik imkoniyatlar, ochiqlik va
moslashuvchanligi bilan bu sohada yetakchilikka intilmoqda.
FOYDALANILGAN ADABIYOTLAR
1.
[Smith, 2020] Smith, J. (2020).
Agricultural Economics: A New Perspective
on Crop Modeling
. Springer.
2.
[Jones et al., 2019] Jones, R., Taylor, M., & Williams, H. (2019).
Climate
Impact on Crop Yield: A Statistical Approach
. Wiley-Blackwell.
3.
[Kuznetsov, 2018] Kuznetsov, A. (2018).
Statistical Modelling in
Agriculture
. Academic Press.
4.
[Davis & Wilson, 2017] Davis, L., & Wilson, R. (2017).
Economic Models
in Agricultural Forecasting
. Oxford University Press.
Acumen:
International Journal of
Multidisciplinary Research
ISSN: 3060-4745
IF(Impact Factor)10.41 / 2024
Volume 2, Issue 4
195
Acumen: International Journal of Multidisciplinary Research
5.
[Petrov et al., 2016] Petrov, A., Ivankov, D., & Korolev, P. (2016).
Regressions in Agricultural Economics
. Routledge.
6.
[Kogan & Choi, 2015] Kogan, F., & Choi, J. (2015).
Smart Farming
Technologies and Their Impact on Yield Predictions
. Elsevier.
7.
[Harrison, 2014] Harrison, G. (2014).
Climate Change and Crop
Productivity: An Integrated Approach
. Springer Nature.
8.
[Miller, 2013] Miller, P. (2013).
Agroinformatics: The Role of Data in
Modern Agriculture
. Springer.
