Monitoring of the Essential Climate Variables of the Atmosphere from Satellite-based Infrared Sounder IKFS-2

Uspensky, A. B.; Rublev, A. N.; Козлов, Д. А.; Golomolzin, Vladimir; Kiseleva, Yu. V.; Kozlov, I. A.; Nikulin, A. G.

doi:10.3103/s1068373922110012

Cited by 5 publications

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This agenda proposes 17 priority goals [5], where the topic "food production" resides in five of this 17 goals: 1) end of poverty, 2) zero hunger, 3) decent work and growth economic, 4) sustainable cities and communities and 5) responsible production and consumption. According to [6][7][8][9][10][11][12][13][14][15], to achieve these five goals, considering the effects of climate change, an efficient tool is the prediction of agricultural crop yield, through multiple linear regressions (MLR) and essential climate variables (ECV). There are 55 ECVs [16], and they stand out for their ease of obtaining and importance in agriculture: 1) average soil moisture (ASM), 2) cumulative effective precipitation (CEP) and air temperature [17,18].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Yield of Irrigated and Rainfed Bean, Based on Essential Climatic Variables, in the Center and South from Sinaloa State, Mexico

Llanes Cárdenas,

Estrella Gastélum,

Parra Galaviz

et al. 2024

Preprint

View full text Add to dashboard Cite

The goal was to model the irrigated (IBY) and rainfed (RBY) bean yields, as a function of essential climatic variables (ECVs), in the center (Culiacán) and south (Rosario) from Sinaloa. In Sinaloa, and for the period 1982–2013 (October–March), the following were calculated: a) temperatures. b) average degree days for the bean, c) cumulative potential evapotranspiration and d) cumulative effective precipitation. For ECVs, from the European Space Agency, e) daily soil moisture. f) IBY and RBY, from the Agrifood and Fisheries Information Service. Multiple linear regressions were applied, which modeled IBY–RBY (dependent variables), as a function of ECVs (independent variables). Then, to establish each Pearson correlation (PC) as significantly different from zero, a hypothesis test was applied: PC vs Pearson's critical correlation (CPC). The four models obtained were significantly predictive: IBY–Culiacán (PC = 0.590 > CPC = |0.349|), RBY–Culiacán (PC = 0.734 > CPC = |0.349|), IBY–Rosario (PC = 0.621 > CPC = |0.355|) and RBY–Rosario (PC = 0.532 > CPC = |0.349|). This study is the first in Sinaloa to predict IBY and RBY based on ECVs, contributing to the production of sustainable food.

show abstract