**2.3 Remote sensing and its application**

*Climate Change and Agriculture*

others management options [13, 14].

used in computer simulation in breeding programs.

**2.2 Weather smart intervention**

*Integrated approach of crop modeling and breeding.*

the planting time, fertilizer dose, planting geometry, cultivar/hybrid and residue management. Increasing number of grains and crop growth rate, re-fitting crop season length by changing growing degree days (GDD) to anthesis and maturity, adjusting grain filling period, decreasing root length of crop are important elements of adaptations in crop model configuration. These elements of adaptations give sound genetic concepts as an important intervention in designing cultivar [4]. Various crop models are being used in improving natural resources to evaluate the impact of future potential climate on crop production [5]. Crop simulation models are appropriate tools for the assessment of crop production options for an environment, including inorganic fertilization levels, plant spacing, planting times and

Development in crop improvement through plant breeding on molecular basis is inadequate by our skill to predict phenotype of plant which based on its genotype, specifically for multifaceted traits [15, 16]. In addition to this, there has been an extended history of designing and application of crop growth and development models for prediction in crop management [17]. The use of such modeling interventions for genotype to phenotype prediction are at beginning [18, 19]. Current studies encouraged that use of crop models have considerable potential to face the genotype-phenotype prediction for application in plant breeding. However, the competence of existing crop models for these type of applications is uncertain [20, 21] and need improvements. The intervention of integration of simulation with plant breeding is an important aspect to design a "virtual cultivar" which can guide to breeders and further recommendation for general cultivation in area. This type of simulation can assemble a virtual variety with acclimatize characteristics for site specific cultivation. Hence, this approach can also elevate the farming to the extent of revolution and have ability to feed the world in safe and healthy style (**Figure 2**). DSSAT (crop modeling) [22], APSIM (crop modeling) are used to assist all type of stakeholders in decision making [23]. PLABSIM (marker-assisted backcrossing) [24], PLABSOFT (plant breeding) [25], QU-GENE (genotype-by-environment interaction) [26] and E-CELL (whole cell simulation) [27] are useful tools being

The risks associated with change in rainfall and temperature at different crop growth stages are directly linked with increase or decrease in pearl millet production. The stakeholders must be linked to automated weather stations and site

**24**

**Figure 2.**

The role of geo-spatial technology is vital in precision agriculture, crop monitoring and yield forecasting. Remote sensing is a potential tool to monitoring crop health and condition. Abiotic stresses; high temperature, insects attack, diseases, moisture deficiencies, fertilizer stress and area affected by these stresses can be detected earlier for quick mitigation. Time series data and maps using remote sensing help to understand the spatial and temporal changes and their drivers. Climate change, biotic and abiotic stresses, and their impacts on crop can be monitored using remote sensing. It depicts and reveals area under crop and its trend over the years. There are many people who are involved in selling, purchasing and pricing of their farm produce. Crop yield forecasting using satellite imagery well before the time of harvest helps to devise policy for crop import and export. Remotely sensed data can be assimilated in crop models to forecast yield on regional scale and for site specific crop production technology [29]. The current innovation in remote sensing sciences is use of higher spatial resolution satellites imagery for precision agriculture and to monitor within field variability. Crop production and management practices can be improved and modified by obtaining information using higher resolution multispectral and hyperspectral data. Use of same input for larger area is wastage of resources and causes crop yield reduction because significance within field variability exists. Pearl millet is grown with diverse distribution in country. Estimation of area, production and average yield through conventional method is a tough job. Remote sensing figures out all these aspects quickly before maturity of crops. Using this type of intervention, we can point out historically, the potential areas, low and high yielding areas of pearl millet with in Pakistan for its retrieving using better management options. Future trends of area and production of field crops can also be predicted using various techniques of remote sensing such as normalize difference vegetation index (NDVI) and random forest as statistical tool which not only assist in decision making for policy makers but also help to evade food insecurity in country [29].
