Ensemble learning prediction of soybean yields in China based on meteorological data

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

“…Jarque-Bera method Finally, the Jarque-Bera test [62] (expression 11) was applied, where the skewness and Kurtosis were used (expressions [12][13].…”

“…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].…”

Modeling Yield of Irrigated and Rainfed Bean in Central and Southern Sinaloa State, Mexico, Based on Essential Climate Variables

“…Specifically for this last goal, beans are a crop that can contribute to reducing world hunger, lowering the total of 2795 million people who will suffer from hunger by the year 2050 [4]. According to [6][7][8][9], to achieve these five goals, considering the effects of climate change, the prediction of agricultural crop yield through multiple linear regressions (MLR) and essential climate variables is an efficient tool. There are 55 essential climate variables [10], of which certain variables stand out for being easy to obtain and important in agriculture: (1) average surface soil moisture (ASM), (2) cumulative effective precipitation (CEP), and air temperature [11,12].…”

“…There are 55 essential climate variables [10], of which certain variables stand out for being easy to obtain and important in agriculture: (1) average surface soil moisture (ASM), (2) cumulative effective precipitation (CEP), and air temperature [11,12]. According to [13,14], from the air temperature it is possible to calculate (3) average maximum temperature (AMT), (4) maximum maximorum temperature (MMT), (5) average minimum temperature (AmT), (6) minimum minimorum temperature (mmT), (7) average mean temperature (AMeT), (8) maximorum mean temperature (MMeT), (9) degree days, and (10) cumulative reference evapotranspiration (CET), these nine essential climate variables being the main ones that affect crop yields. According to [15], the crops most sensitive to extreme essential climate variables conditions in Latin America are corn, wheat, and bean.…”

“…The scarcity of IBY modeling studies is due fundamentally to the complications of obtaining daily data on irrigation depths and exact precipitation that the crop uses for proper growth [3,18]. The incomplete IBY-RBY and climate series [8,18], which usually do not exceed the statistical threshold for modeling [7], represent another limitation for the creation of sensitive models to predict IBY-RBY. Specifically in Sinaloa, the sulfur bean is the most cultivated bean [19], occupying a large proportion of the annual demand (120,000 tons), obtained with a harvest of 74,000 hectares (average yield of 1.66 t ha −1 ) [20].…”