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Indonesian Makalah ini membahas kecenderungan dampak pemanasan global yang terjadi akhir-akhir ini. Estimasi dilakukan dengan parameter fraksional dari catatan relatif panjang menggunakan tehnik outlier aditif sebagai pengamatan bebas yang dihasilkan di atmosfer karena pemanasan global. Selanjutnya penelitian ini mengamati secara empiris dampak pemanasan global terhadap aspek tertentu produksi pertanian global. Berdasarkan simulasi Monte Carlo, proses menghasilkan data diterapkan dimana outlier aditif dihasilkan melalui cara discrete atau tidak kontinyu. Hasil observasi menunjukkan bahwa outlier aditif mempengaruhi bias dan MSE parameter fraksional estimasi. Ukuran outlier aditif dalam proses menghasilkan data juga memiliki pengaruh penting terhadap parameter farksional estimasi yang tergantung pada nilai parameter fraksional yang sebenarnya. Hasilnya menunjukkan variabilitas non tren atau siklus alami yang dipengaruhi oleh proses stokastik dalam hal sifat perubahan iklim dengan observasi bebas (outlier) yang menghasilkan outcome berlawanan dari ketidakpastian yang intensif terhadap tren data temperatur dunia pada kondisi riil. Hasil pengamatan empiris menyimpulkan bahwa pada akhir abad 21 secara meyakinkan pemanasan global akan mempunyai dampak negatif terhadap agregat produksi pertanian global dan dampaknya bisa sangat parah jika manfaat fertilizasi karbon (peningkatan hasil dalam lingkungan yang kaya karbon) tidak tampak, terutama jika kelangkaan air membatasi irigasi. Lagi pula, jika pemasan global tidak berhenti pada tahun 2080, tetapi temperatur global terus meningkat pada abad 22, kegagalan produksi pertanian bisa semakin parah. Studi ini juga menunjukkan bahwa akumulasi pengaruh produksi pertanian kemungkinan lebih serius bagi negara berkembang dengan kerugian terbesar di Afrika, Amerika Latin, dan India. English This paper primarily attempts to detect the trend in the present upshots of global warming temperature data. It has been done through the estimation of the long memory fractional parameter using a simulation technique in the presence of additive outliers which stands as wild observations generated in the atmosphere due to global warming. Then, the study investigates empirically the impact of global warming on the particular aspect of global agricultural production. Based on Monte Carlo simulations, a data generating process is applied where additive outliers are generated in a discrete way. Observed facts reveal that additive outliers affect the bias and the MSE of the estimated fractional parameter. The size of the additive outliers in data generating process has also important effects on the estimated fractional parameter depending on the value of true fractional parameter. The result exhibits a non-trend or a natural cyclical variability influenced by a stochastic process in the case of climate change behavior with wild observations (outliers) that produce a contradictory outcome of profound uncertainties against the case of true world temperature data trend. The results of empirical investigations assert that in the late 21st century unabated global warming would have a negative impact on global agricultural production in the aggregate and the impact could be severe if carbon fertilization benefits (enhancements of yields in a carbon-rich environment) do not materialize, especially if water scarcity limits irrigation. In addition, if warming would not halt in the 2080s, but would continue on a path toward still higher global temperatures in the 22nd century, agricultural damage could be more severe. The study also shows that the composition of agricultural effects is likely to be seriously unfavorable to developing countries with the most severe losses in Africa, Latin America and India.

Uncertainties in Global Warming Temperature-Trend and Their Impacts on Agricultural Production: an Econometric Evaluation