Modeling of climate parameters with planting area and pest attacked area on shallots for the development of early warning systems and horticultural cropping schedules

One way to mitigate the decrease in shallot production during the off-season is preparing an early warning system and the management of crops planting. This system needs a mathematical model to assist in decision-making. This paper presents a modeling of the relationship between rainfall and planting area and pest attacked area of shallot. The results of the modeling analysis between rainfall and planted area illustrate that an increase of rainfall up to 137.5 mm/month in the lowland area at Brebes, or up to 247.0 mm/month in rainfed or upland area at Garut, is correlated with an increase in the planting area, however then above that rainfall intensity, an increase of rainfall is correlated with a decrease in the planting area. Regression analysis between the Spodoptera exigua attacked the area of the shallot and climate variables illustrates that the attacked area of the shallot decreases if there is an increase in the minimum temperature up to 25.5 °C. Conversely, an increase in the S. exigua attack area of shallot occurs if there is an increase in the duration of solar irradiation of up to 8 hours/day or an increase in a means air humidity of up to 77%. Regression analysis between the Trotol attack area and climate variables illustrates that the Trotol attack area decreases if there is an increase in the average wind speed of up to 5.7 knots. Conversely, an increase in the attacked area of Trotol occurs if there is an increase in rainfall of up to 205 mm/month or an increase in a means air temperature of up to 29.0 °C. This illustrates that climate conditions that are sunny, cool, dry, and calm can reduce the area of attack by S. exigua and Trotol. The threshold values obtained through the above analysis are then used in decision support in the EWS SIPANTARA, namely the Early Warning System and Planting Schedules Management for Horticulture.


Introduction
The agricultural industry in Indonesia is facing significant challenges as a result of climate change and variability.The diversity of geography and climate patterns in Indonesia led to a wide range of climatic events, including temperature shifts, altered precipitation patterns, and an increase in extreme weather 2 events that caused floods and droughts.These changes can disrupt agricultural systems, impacting crop growth and productivity, particularly through their influence on harvest yields.To address these challenges, advanced information system mechanisms that use climate data monitoring and prediction are essential to assist farmers in anticipating and mitigating the effects of extreme weather events.Some innovations in information systems have been developed for rice, maize, and soybean [1][2][3], or plantation crops [4,5].These systems provide information about rainfall season, planting schedule, pest vulnerability prediction, high-risk periods for plants, crop water requirement, irrigation schedule, and so on, based on rainfall prediction [6,7].However, for horticultural commodities, there is still a need for a similar system that is expected to serve as a planting reference and early warning tool for farmers.
Some of the strategic commodities of horticulture are shallot and chilies.Shallot and chilies are unique and important commodities in Indonesian cuisine.Shallot price fluctuations can affect economic strength through inflation and deflation in the Indonesian food sector.Changes in the price of shallots can also impact the cost of other foodstuffs, resulting in increased overall prices of goods and services, leading to a higher inflation rate [8][9][10].On the field, shallots are also the plant that vulnerable to pest and climate variability, such as flood and drought [11], as well as chili production fluctuates and the fluctuations are caused by weather elements [12,13].
Shallot production during planting season in Indonesia requires a holistic and adaptive approach considering the unique climatic conditions.The range of shallot cultivation is quite wide, from lowlands until highlands [14] and shallots are commonly farmed in diverse weather conditions, including warm climates in open fields and cold climates with moderate rainfall [15].By combining traditional knowledge and modern farming techniques, farmers can maximize yields and contribute to the availability of this strategy commodity for both local consumption and the wider market.Shallot production during the planting season also needs to be supported by production during the off-season, however [16] said that cultivating shallots during off-season frequently results in challenges, leading to reduced yields and profits for farmers.While off-season shallot production demands increased investments in terms of technology, resources, and expertise, it offers the potential for higher market prices due to reduced supply during this period.By harnessing innovation, sound agricultural practices, and adaptability, farmers can navigate the challenges of off-season cultivation and contribute to a more resilient and diversified food supply [8][9][10].
Currently, the Ministry of Agriculture is developing an information system called the Early Warning System and Planting Schedules Management for Horticulture (EWS SIPANTARA).The system is expected to be the reference for farmers to reduce the risk of the impact of climate change, natural disasters, and pest damage on their crops.This system can serve as the basis for every policy decision for shallot, red chili, and pepper commodities.
To provide scientific reinforcement in the system, threshold values and some mathematical models are needed to help make decisions.This paper presents a modeling of the relationship between climate variables and planting area and pest attack area of shallot.

Methodology
The paper is part of the design of an early warning system and information on planting schedules for horticultural crops called EWS SIPANTARA.EWS SIPANTARA was designed as an early warning system and information system that informs about the potential for flooding, drought, or the emergence of pests as well as predicting the optimum planting schedule for shallots, red chilies, and cayenne peppers.The flow of data, analysis, and information starts from collecting data, including the area of plant damage due to floods, droughts, and pests, rainfall predictions, dry land area, and information on alternative water sources.The analysis includes system design, data management, modeling, decisionmaking, and presenting web-based analysis results.Followed by dissemination to users and access to information in the system by users.Developing a model of the relationship between parameters is needed to support decision-making in the system, as stated by Czembor et al. [17] that the relationship between parameters is crucial in agricultural studies and decision-making for crop monitoring.The paper 3 presented modeling between rainfall parameters with the area of damage caused by pests and the planting area of shallots.

Data compilation and preparation
The data or information collected includes rainfall data and prediction, damaged areas of pests, planting areas, and dryland areas.The rainfall data was obtained from the Climate Change Information Center of the Meteorology, Climatology, and Geophysics Agency.Information on damaged areas of pests was obtained from the Directorate of Horticulture Protection, planting areas were obtained from The Center of Agricultural Data and Information, and dryland areas were obtained from the BPSI Agroclimate and Hydrology.The data is then processed in such a way that it is tabularly arranged based on sub-district or district.

Simple or multiple regression analysis
Regression analysis is carried out to obtain information on the mathematical relationship between one dependent parameter, in this case, the area of plant damage due to pests, and one or several independent variables, in this case, the climate parameters.The mathematical equation produced through regression analysis in general is: where  ̂ represents the predicted value of pest-damaged area, y represents the observed value of pestdamaged area, b1, b2, …, b is the regression coefficient, x1, x2, …, x is the predictor in this case, the climate parameters, and ε is a random error, ε ∼ N(0, 2 ) [18].The steps taken in generating equations between parameters include (1) creating a distribution plot of the analyzed data, (2) determining the equation model, (3) determining the equation coefficients, and (4) analyzing the correlation coefficient and variance of the resulting equation.

Two-phase regression analysis
In certain cases, the scatter plot results illustrate that the data distribution is inadequate if analyzed using a simple regression model because different data intervals have different trends.In this kind of case, modeling is carried out using a 2-phase regression analysis, as follows: where   is the zero-mean independent random error with a constant variance.This allows for both stepand-trend-type changepoints.The time c is called a changepoint if a1 ≠ a2 (step type) and/or b1 ≠ b2 (trend type) [19,20].

Relationship between climate parameter and shallot pest-damaged area
The relationship between climatic parameters and the extent of infestations by Spodoptera exigua and Alternaria porri on shallots demonstrates a non-linear association (Figure 1 and Figure 2).Regression results reveal that the correlation between Spodoptera exigua and relative humidity (RH) is notably higher compared to minimum temperature and duration of sunlight exposure.The attacked area will decrease if there is an increase in the minimum temperature up to 25.5 OC.Conversely, an increase in the S. exigua attack area of shallot occurs if there is an increase in the duration of solar radiation of up to 8 hours/day or an increase in a means air humidity of up to 77%.Similarly, rainfall exhibits a stronger correlation with Alternaria porri compared to average temperature and wind speed.Based on the aforementioned outcomes, it can be inferred that the extent of infestations by Spodoptera exigua and Alternaria porri on shallots tends to be high at specific values, and these values could be considered as their respective thresholds.The A. porri attack area decreases if there is an increase in the average wind speed of up to 5.7 knots.Conversely, an increase in the attacked area of A. porri occurs if there is an increase in rainfall of up to 205 mm/month or an increase in a means air temperature of up to 29.0 O C.

Relationship between rainfall intensity and shallot planting area
In the realm of agriculture and climate science, the relationship between rainfall intensity and the expansion or reduction of planting areas stands as a pivotal determinant of yield and land utilization strategies.This correlation, however, is far from uniform in all situations, as it manifests through a complex interplay of multifarious factors.Central to this dynamic are considerations such as plant type or variety related to plant water requirements [21], existing soil characteristics with respect to percolation, and the effectiveness of irrigation and drainage systems [22].Additionally, the broader climatic context within which planting occurs exerts a profound influence on the nature and pattern of this relationship, as it will also relate to other climate variables.Therefore, to understand the correlation between rainfall intensity and planted area for a particular crop in different geographical locations, rigorous empirical research emerged as an indispensable imperative.Given the diverse nature of these variables, scientific investigations tailored to plant varieties and regional contexts remain fundamental to advancing our understanding of the relationship between these two variables, thereby informing the adaptation strategies and agricultural cultivation patterns that should be adopted.
To examine the relationship between rainfall intensity and the shallot planting area, analyses were conducted at two locations with distinct geographical conditions representing shallot cultivation regions: Brebes, which represents a lowland area, and Garut, a highland region.The scatter plot in Figure 4(a) illustrates the correlation between rainfall intensity (Rf) and the area of shallots planted (A) in Brebes, while Figure 4(b) shows the same relationship in Garut.The analysis in both locations revealed a twosegmented correlation (2-phase regression), indicating that the relationship between the two variables is not constant but exhibits breakpoints or thresholds that alter the relationship [22].In this context, Figure 3 illustrates an initial positive correlation, signifying that an increase in rainfall is initially followed by an increase in the planting area.However, after reaching breakpoints, increasing rainfall leads to a reduction in the planting area, as indicated by the negative slope.The graph distinctly highlights a breakpoint at a rainfall intensity of 137.5 mm/month in Brebes and 247.0 mm/month in Garut.Below these values, there exists a positive correlation between rainfall intensity and the shallot planting area, as denoted by the red equation.This suggests that as rainfall increases, so does the shallots planted area, up to a certain threshold.Beyond this breakpoint, the correlation becomes negative, as represented by the blue equation.This signifies that beyond this threshold, increased rainfall is associated with a decrease in the shallot planting area.This breakpoint likely represents the maximum beneficial amount of rainfall for shallot growth.Above this point, excessive rainfall can result in adverse conditions such as flooding, waterlogging, or increased prevalence of diseases as described in the previous subchapter, ultimately reducing the feasibility of shallot cultivation over a larger area.This data indicates that the upland region of Garut exhibits a higher tolerance level towards rainfall intensity, as evidenced by the larger breakpoint value, compared to the lowland area of Brebes.Lowland areas are more susceptible to inundation or flooding due to their proximity to the water table, the level beneath which the soil becomes saturated with water.During heavy rainfall, the water table can rise, leading to flooding [23,24].Regarding shallots, excessively moist soil conditions can give rise to issues such as root rot and bulb decay, which hinder growth and increase the risk of disease infection [25].
This two-phase relationship is a common pattern between rainfall and planting area, with the differentiating factor being in breakpoint values.The value of this breakpoint depends largely on the location (related to climate and geography), the type of soil (related to percolation, and the conditions of irrigation and drainage systems), and the type or variety of plant.This breakpoint value serves as the ideal rainfall threshold.
In the development of climate-related early warning systems, information regarding these breakpoint values as ideal rainfall thresholds becomes critical.This is because it relates to the point where if the predicted rainfall intensity exceeds the breakpoints, it has the potential to decrease the planting area and, consequently, lead to reduced production.Therefore, this information forms the basis for determining the status of a region and the strategies that need to be prepared to address varying rainfall intensities.

Model integration for decision-making in the system
One of the things needed to make decisions in EWS SIPANTARA is the need for information on the optimum conditions of the pest which underlies the early warning system to anticipate optimum conditions, as well as implementing a planting schedule when potential rainfall conditions are obtained based on predictions of future climate parameters.Bearing in mind that the analysis carried out is resource-based, the optimum conditions approach is carried out by looking at the range of climate variables which produces a distribution of dependent variable values of 60-80%.This approach produces the following roles: (1) Optimum minimum air temperature for S. exigua is <24.0 O C or >26.6 O C,

Figure 1 .
Figure 1.Data, analysis, and information flow diagram in EWS SIPANTARA.

Figure 2 .
Figure 2. The result of multiple regression analysis between S. exigua damaged area versus air temperature, solar radiation, and air humidity.

Figure 3 .
Figure 3.The result of multiple regression analysis between A. porri damaged area versus wind speed, rainfall, and air temperature.

Figure 4 .
Figure 4.The result of a 2-phase regression analysis between rainfall intensity and planted area of shallot, (a) Brebes for lowland area, and (b) Garut for highland area.