Temperature Based Agrometeorology Indices Variability in South Punjab, Pakistan

Abstract

Climate change has a major impact on crop yield all over the world. Pakistan is one of the major affected countries by climate change. The agrometeorology indices were determined for the South Punjab region, which is a hot spot for climate change and food security. This region is rich in agriculture, but crop yield relationship is estimated with agrometeorology indices (AMI). Temperature stress (33°C), average diurnal temperature range (12°C), Average accumulative growing degree days (1303°C), phototemperature (27°C) and nyctotemperature (21°C) indices were determined for Multan. The variation in diurnal temperature was found at 0.39 for Bahawalpur region and similar variation was observed in growing degree days, which is 0.11 more than the diurnal temperature range. The extreme of these indices which influence the crop yield was found in May and June. The cropping period from sowing to harvest varied due to climate change and cause to decrease in the yield of the crop. The indices are regarded as crop performance indicators. So, policymakers and agricultural scientists should take necessary measures to mitigate such kinds of challenges.

1. Introduction

During the last decade, climate change has raised an international attention due to concerns of negative impacts on ecosystems, agriculture, water supply and management, human welfare and regional political stability [1, 2]. The global situation is well described by the IPCC assessment report [3], which in particular cites global mean temperatures as having risen approximately by 0.74°C in the last 100 years. The majority of this warming has occurred since 1950, most likely due to increasing greenhouse gas concentrations to unprecedented levels in recent history [4]. Global climate models (GCMs) predict temperature increases of 1.1–6.4°C over 1990–2100 [3]. Due to the great influence of temperature on evapotranspiration and precipitation for soil water availability and drought events, climate change might have an immediate, direct effect on vegetation and crop productivity and, therefore, on the related net income for farmers [1, 5, 6].

Climate change is one of the most prominent global environmental issues. During the period from 1885 to 2012, the mean global temperature has increased by 0.85°C and is predicted to increase further by 1.6–5.8°C by the end of 21st century [3]. Climate is one of the most critical limiting factors for agricultural production: frost risk during the growing period and low and irregular precipitation with high risks of drought during the cultivating period are common problems in agriculture. In recent years a change in climate has been documented in many locations throughout the world. Increasing rainfall trends were reported in Argentina [7], Australia and New Zealand [8, 9]. The minimum temperature increased almost everywhere. The maximum and mean temperature increased in northern and central Europe, over the Bulgaria [10], Canada [11], Australia, New Zealand [8], India [12]. These results support the assumptions [13] that mid-latitude regions such as the mid-western USA, southern Europe and Asia are becoming warmer and drier, whereas the lower latitudes are becoming warmer and wetter.

Developing countries are more vulnerable to such changes as they have limited resources to cope up with the disasters and agriculture plays dominant role in their national economy [14]. The northern part of Indian sub-continent has been placed under high risk zone for heat stress risks in view of future climate change scenarios [15]. Under such conditions, the sustainability of natural resources and food security for the increasing population in the region is at risk. An increase, even moderate in global temperature is expected to result in a change in frequency of extreme weather events like drought, heavy rainfall and storms [16]. Small changes in precipitation mean result in a relatively high increase in the probability of precipitation extremes [17, 18]. The same effect has been demonstrated for temperature changes [19]. Wagner [20] suggest that the frequency of extreme events is relatively more dependent on a climate change as compared to mean values of climatic parameters.

Ali et al., [21] also determined the future trends of temperature in different regions of Pakistan. Nadeem et al. [22] found the results using different climate models in south Punjab, Pakistan. The study period divided into base line period (1980–2018) and found the increase in average temperature 0.94°C for this period. According to Global Climate Models increased in temperature 3–5°C up to 2050. The yield of different crops decreased and sowing 18–25 days earlier as adaptation startgey to cope this climate change challenge. Punjab is also experiencing large fluctuations in temperature and precipitation patterns every year leading to large oscillations in agricultural productivity in the region. The unpredictable weather conditions have already started to diverse impact on productivity of wheat crop during last 5–6 years. To manage this alarming situation, there is a need to analyze the spatiotemporal variability of climatic conditions at regional scale so that viable mitigation/adaptation strategies could be developed and implemented on regional basis. Keeping in this view, the spatiotemporal climatic variability during kharif and rabi seasons has been studied for three different agroclimatic regions of South Punjab by using statistical approaches.

2. Materials and methods

Daily minimum (T_min) and maximum (T_max) temperature from two stations in South Punjab (SP), Pakistan (Table 1, Figure 1) with the longest and complete records were used. First, monthly variations were identified in temperature series considering the study period. Multan and Bahawalpur stations were chosen for the time of 2012–2016. Both divisions comprised a major part of South Punjab. The region is considered very important regarding the agriculture production. Wheat and Cotton crops were dominated for the South Punjab region (Table 1).

Station	Average	Median	Standard deviation	Variance	skewness	Co-efficient of variation
	Maximum Temperature
Multan	31.5	32.5	8.27	68.32	−0.31	0.26
Bahawalpur	32.1	33.8	8.08	65.26	−0.44	0.25
	Minimum Temperature
Multan	19.6	20.80	8.83	77.99	−0.26	0.45
Bahawalpur	19.10	20.20	8.56	73.32	−0.44	0.45

Table 1.

Statistics of minimum and maximum temperature.

Figure 1.

Location of study area.

3. Methods

Determination of following temperature base agro metrological indices has carried out. Crops growth when the daily T_mean is above a given temperature threshold. Different temperature base indices are used to quantify that climate change process and its effect on crop production area and yield. The temperature base agro meteorological indices are given in Table 2.

Indices	Abbreviation	Description	Unit
Diurnal Temperature Range	DTR	Difference between daily maximum and minimum temperature	°C
Growing Degree Days	GDD	Accumulated number for temperature degrees above 30°C	°C
Temperature stress >30°C	T30	Average Temperature threshold above 30°C	°C
Nyctotemperature	Tn	Mean temperature during night when light levels are limited	°C
Phototemperature	Tb	Effective light temperature	°C

Table 2.

Temperature base agro-meteorological indices.

The maximum, minimum and average temperature of South Punjab is extreme in month of May and June (Figure 2). Month of July and August is identified lower as compared to extreme months due to moon soon spell.

Figure 2.

Average, maximum and minimum temperature of Multan and Bahawalpur.

To explore the variations of temperature during the months, the Diurnal Temperature Range (DTR) [23] is determined:

$$DTR\left({}^{°}C\right)=T_{\text{max}}-T_{\text{min}}$$E1

Chilling temperature is most important during the dormancy period. It also plays key role in the growth of plants in spring season. Deciduous fruit trees demanded the accumulating chilling temperature in temperate environment.

When the daily temperature exceeds from threshold temperature. Threshold temperature can vary crop as well as species and stages of crop. Quantify the thermal time and growing degree day used to estimate the process. It also helps for selection of crops and hybrid varieties to achieve the maximum growth at the time maturity and yield. The growing degree day is determined [24, 25]:

$$GDD={\displaystyle \sum }_1^n\left(T_{\mathit{aver}}-T_b\right)$$E2

Where, n is the number of days in each month, T_mean is the daily average temperature computed using daily T_min and T_max, T_b is threshold temperature for the crop growth.

After decided the analyses of growing degree days, the heat unit is most important indices for the crops. The cumulative heat unit is calculated by summing the daily mean temperature above the base temperature [26]: The effective light temperature estimated from the given equation:

$$T_P=T_{\text{max}}-0.25DTR$$E3

This index is computed for the different stages and significance for mean temperature at daytime. It is named as Phototemperature index [27]. There is also other index to compute the effect of mean temperature at full nighttime:

$$T_n=T_{\text{min}}+0.25DTR$$E4

It is named as nyctotemperature index for given crop during the different stages. There are also identify the maximum and minimum temperature of the year highlighted for this region and alarm all the stakeholder regarding the climate change warming.

4. Results and discussions

The results reveled that different agro-meteorology indices varied whole the time period for this region. The variations diurnal temperature range is more as highlighted from coefficient of variations. These variations of Diurnal temperature range observed 5% higher in Multan as compared to Bahawalpur. Growing degree day of Multan 2% higher than Bahawalpur. Temperature stress play important role for the crop growth and phenology at different stages, South Punjab region have rich in agriculture and playing key role textile industry. Textile industry raw materials produced from this region because it cottons dominant region.

The temperature above the 30°C, this index found to be similar for both stations. Phototemperature index Multan is higher 5% than Bahawalpur (Table 3). The variations in Nyctotemperature of Bahawalpur index lower 2% higher than Multan. The interanual variations in diuneral temperture range found extreme in month of May and June. The lowest DTR highlighted in month of October. The higher variability indicated from diuneral temperture range during the different months of 2012–2016 (Figure 3).

Indices	Average	Median	Cv
Multan
DLR	12	12.4	0.41
GDD	1303	1331	0.6
T30	33.7	33.4	0.1
Tn	21.4	23.30	0.45
Tp	27	28.79	0.38
Bahawalpur
DLR	13	13.5	0.39
GDD	1277	1460	0.5
T30	33.5	33.5	0.10
Tn	21.2	23.10	0.44
Tp	27.4	29.84	0.36

Table 3.

Descriptive statistics of agrometeorological indices of South Punjab.

Figure 3.

Diurnal temperature range for study period.

Heat stress found to be maximum in month of June for period 2012–2016 (Figure 4). Temperature stress of Multan was 1.5% higher than Bahawalpur for the month of June. The variations in heat stress found to be more in Multan as compared to Bahawalpur. For month of April, heat stress was 32.10°C in Bahawalpur, which was 0.7°C more from Multan. Maximum variance in heat stress in month of May were noticed for Multan. Similar, minimum variance determined in month of September for Bahwalpur. The variation of heat stress determined in month of May and June for both stations. These results consistent with the findings of [28].

Figure 4.

Temperature stress above threshold for study period.

Accumulative growing degree days increased after the month of June. Multan found to be higher from June to December but lower during the month January to May (Figure 5). It can be found the matching point of both stations occurred in month of May. The accumulative growing degree day become constant in month of November and December for both stations, but Multan found to be 8% more than Bahwalpur. Minimum growing degree day ware in month of May for Bahawalpur and month January as well as March for the stations of Multan [25].

Figure 5.

Growing degree days indices of study area.

Phototemperature and Nyctotemperature index were determined for this region. May and June are critical month for temperature-based agriculture index. August and September have decreased variations due to moon soon effect. Winter seasons found to be lower regarding the temperature based agro-meteorology index. The extreme event of both index Bahawalpur is June with different amount. For Multan, phototemperature and nyctotemperture index found to maximum in month June with 18% variations. Both indices observed minimum in month of January, which is lower 71% and 57% respectively. Overall, month of May and June found to be maximum and minimum in month December and January respectively as shown in Figures 6 and 7.

Figure 6.

Phototemperature and Nyctotemperature index for Bahawalpur city.

Figure 7.

Phototemperature and Nyctotemperature index for Multan city.

5. Discussions

In this study, we analyze the different agro-meteorology index, which is based on maximum and minimum temperature. Over all these indices indicate global warming decrease the average yield of wheat crop and cotton yield increase in this region. Brar and Singh [29] determined the relationship of agro-meteorology indices (AMI) and cotton yield, the experiments was conducted in north western part of India. The water scarcity was major challenge for cotton production. The crop yield were found increased with higher AMI with heavy pre-sowing irrigation and early sown crop at irrigation with four weeks. The duration to achieve the different phenological stages of wheat with different irrigation treatments for significant higher yield and growing degree days for Rajasthan, India [30]. The uneven trends in temperature indices in Serbia were found the period of 1961–2010. The temperature for growing seasons were complex pattern. The examined temperature indices were found the cooling tendency during the growing seasons and a warming tendency during the dormancy. The warming tendency for the period of 1981–2010 was detected in both seasons with similar magnitude [31]. Jan et al. [32] examined the variability in biological yield of crop with growing degree days with regression model. Fernández-Long et al. [23] investigated the trends in Argentina and agro-meteorology indices identified the potential effect of weather on crop yield and arise a challenge for management decisions. Lalić et al. [1] also highlighted the relation between agro-metrological conditions and crop yield through modeling in different countries. Capra et al. [5] determined the maximum, minimum and mean temperature variability in Italy and found to be very complex behavior with crop growth as well as production. Choudhary et al. [33] also determined the agro-meteorology indices for India and found the relation with crop. These indices identified the crop performance as indicator.

The crop yield and indices found to be closely related. The extreme temperature influence on grain yield and growth of the crop. Due to rapid GDDs shorten the span life of crop and reduce the yield. From this study, the crop yield (Figures 8 and 9) is reducing to due to increase in temperature as well as temperature base agro-meteorology indices, which is indicator of challenge of wheat crop in southern part of Punjab and other hand cotton crop is arising. These results were found to be consist with [34, 35]. The temperature stress, phototemperature and nyctotemperture index were also same behavior for crops as findings. Shaheen et al. [36] investigated the GDDs and estimated the shorten the length of growing seasons of crop, which ultimately affect the crop yield, which is consistent with current study. You et al. [37] determined the crop yield and temperature increase relationship for China. The increase in temperature 1°C reduce the yield 3–10% for mainland of China. The wheat yield decreased as compared to initial year, while cotton yield increase for the same period for region of South Punjab.

Figure 8.

Wheat yield for the study period.

Figure 9.

Cotton yield for study period.

6. Conclusion

From this study, it can be concluded that there is a necessity of management and applications of these indices to allocate inter-comparison of the significant results and to improve the understanding. The threshold temperature 30°C identified through this study. It will be helpful for developing whether calendar of wheat, crop insurance product and breeding temperature stress resistant genotype of crops for South Punjab, Pakistan. Month of May and June are more important for this region due to extreme values of temperature base index occurs in these months. These agro-meteorology indices demand to investigate it local and regional basis. There is also necessary to investigate at different time scale i.e., short, and long term. The extreme temperature impact on crop production and management should be locally assessed. Due to extreme temperature base agro-meteorology index in South Punjab, extreme weather is becoming great challenge for farmer community, which effect the livelihood of this region. It creates food security challenge for this region. Due to extreme variations in agro-meteorology stresses, the new varities of crop is needed to introduce which are suitable with current variations in climate. Due to increase in temperature, there is also needed to cope the water scarcity challenge in this region. Therefore, economically water cheap technique should be introduced in this region. The policy maker and scientific community need to take measures to cope with this challenge.