Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

This study aimed to develop and evaluate data driven models for prediction of forest yield under different climate change scenarios in the Gallies forest division of district Abbottabad, Pakistan. The Random Forest (RF) and Kernel Ridge Regression (KRR) models were developed and evaluated using yield data of two species (Blue pine and Silver fir) as an objective variable and climate data (temperature, humidity, rainfall and wind speed) as predictive variables. Prediction accuracy of both the models were assessed by means of root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (r), relative root mean squared error (RRMSE), Legates-McCabe’s (LM), Willmott’s index (WI) and Nash-Sutcliffe (NSE) metrics. Overall, the RF model outperformed the KRR model due to its higher accuracy in forecasting of forest yield. The study strongly recommends that RF model should be applied in other regions of the country for prediction of forest growth and yield, which may help in the management and future planning of forest productivity in Pakistan.

Clustering and Smoothing Pipeline for Management Zone Delineation Using Proximal and Remote Sensing

In precision agriculture (PA) practices, the accurate delineation of management zones (MZs), with each zone having similar characteristics, is essential for map-based variable rate application of farming inputs. However, there is no consensus on an optimal clustering algorithm and the input data format. In this paper, we evaluated the performances of five clustering algorithms including k-means, fuzzy C-means (FCM), hierarchical, mean shift, and density-based spatial clustering of applications with noise (DBSCAN) in different scenarios and assessed the impacts of input data format and feature selection on MZ delineation quality. We used key soil fertility attributes (moisture content (MC), organic carbon (OC), calcium (Ca), cation exchange capacity (CEC), exchangeable potassium (K), magnesium (Mg), sodium (Na), exchangeable phosphorous (P), and pH) collected with an online visible and near-infrared (vis-NIR) spectrometer along with Sentinel2 and yield data of five commercial fields in Belgium. We demonstrated that k-means is the optimal clustering method for MZ delineation, and the input data should be normalized (range normalization). Feature selection was also shown to be positively effective. Furthermore, we proposed an algorithm based on DBSCAN for smoothing the MZs maps to allow smooth actuating during variable rate application by agricultural machinery. Finally, the whole process of MZ delineation was integrated in a clustering and smoothing pipeline (CaSP), which automatically performs the following steps sequentially: (1) range normalization, (2) feature selection based on cross-correlation analysis, (3) k-means clustering, and (4) smoothing. It is recommended to adopt the developed platform for automatic MZ delineation for variable rate applications of farming inputs.

Validation of Rapid and Low-Cost Approach for the Delineation of Zone Management Based on Machine Learning Algorithms

Proximal soil sensors are receiving strong attention from several disciplinary fields, and this has led to a rise in their availability in the market in the last two decades. The aim of this work was to validate agronomically a zone management delineation procedure from electromagnetic induction (EMI) maps applied to two different rainfed durum wheat fields. The k-means algorithm was applied based on the gap statistic index for the identification of the optimal number of management zones and their positions. Traditional statistical analysis was performed to detect significant differences in soil characteristics and crop response of each management zones. The procedure showed the presence of two management zones at both two sites under analysis, and it was agronomically validated by the significant difference in soil texture (+24.17%), bulk density (+6.46%), organic matter (+39.29%), organic carbon (+39.4%), total carbonates (+25.34%), total nitrogen (+30.14%), protein (+1.50%) and yield data (+1.07 t ha−1). Moreover, six unmanned aerial vehicle (UAV) flight missions were performed to investigate the relationship between five vegetation indexes and the EMI maps. The results suggest performing the multispectral images acquisition during the flowering phenological stages to attribute the crop spatial variability to different soil proprieties.

Wheat yield prediction based on weather parameters using multiple linear, neural network and penalised regression models

Wheat yield production is largely attributed by weather parameters. Model developed by multiple linear, neural network and penalised regression techniques using weather data have the potential to provide reliable, timely and cost-effective prediction of wheat yield. Wheat yield data and weather parameter during crop growing period (46th to 15th SMW) for more than 30 years were collected for study area and model was developed using stepwise multiple linear regression (SMLR), principal component analysis (PCA) in combination with SMLR, artificial neural network (ANN) alone and in combination with PCA, least absolute shrinkage and selection operator (LASSO) and elastic net (ENET) techniques. Analysis was carried out by fixing 70% of the data for calibration and remaining dataset for validation. On examining these models, LASSO and elastic net are performing excellent having nRMSE value less than 10 % for four out of five location and good for one location, because of prevention in over fitting and reducing regression coefficient by penalization.

Long-Term Monitoring of Different Field Traffic Management Practices in Cereals Production with Support of Satellite Images and Yield Data in Context of Climate Change

Cereals in Europe are mainly grown with intensive management. This often leads to the deterioration of the physical properties of the soil, especially increasing bulk density due to heavy machinery traffic, which causes excessive soil compaction. Controlled traffic farming (CTF) technology has the potential to address these issues, as it should be advantageous technology for growing cereals during climate change. The aim of this study was to compare the yield potential of CTF and standardly used random traffic farming (RTF) technology using yield maps obtained from combine harvester and satellite imagery as a remote sensing method. The experiment was performed on a 16-hectare experimental field with a CTF system established in 2009 (with conversion from a conventional (ploughing) to conservation tillage system). Yield was compared in years when small cereals were grown, a total of 7 years within a 13-year period (2009–2021). The results show that CTF technology was advantageous in dry years. Cereals grown in the years 2016, 2017 and 2019 had significantly higher yields under CTF technology. On the contrary, in years with higher precipitation, RTF technology had slightly better results—up to 4%. This confirms higher productivity when using CTF technology in times of climate change.

Pre-extension demonstration of field pea varieties in selected districts of Bale highlands, Oromiya national regional state, Ethiopia

Pre extension demonstration of improved field pea varieties was conducted in Goba, Sinana and Agarfa districts of Bale zone. The main objective of the study was to demonstrate and evaluate recently released (Weyib) variety along with standard check. The demonstration was under taken on single plot of 10mx10m area for each variety with the spacing of 30cm between rows and recommended seed rate of 75kg/ha and fertilizer rate of 100kg/ha NPS. Mini-field day involving different stakeholders was organized at each respective site. Yield data per plot was recorded and analysed using descriptive statistics, while farmers’ preference to the demonstrated varieties was identified using focused group discussion and summarized using pair wise ranking methods. The demonstration result revealed that Weyib variety performed better than the standard check (Tulu shanan variety) with an average yield of 34.47qt/ha, while that of the standard check was27.26qt/ha. Weyib variety had 17.27% yield advantage over the standard check. Thus, Weyib variety was recommended for further scaling up. Res. Agric., Livest. Fish.8(3): 267-271, December 2021

Field Bio-efficacy and Residue Dynamics of the Fungicide Polyoxin D Zinc Salt 5% SC in Grape (Vitis vinifera)

The field experiments were carried out to evaluate the bio-efficacy and residue dynamics of Polyoxin D Zinc salt 5% SC in grape during 2014–2015 and 2015–2016 at ICAR-National Research Centre for Grapes, Pune. Polyoxin D Zinc salt 5% SC @ 600 ml ha-1 gave the best control of the disease, both in the leaves and bunches with a percent disease control of 56.4 and 75.7 respectively, as compared to untreated control. The percent disease control of the test fungicide Polyoxin D Zinc salt 5% SC @ 600 ml ha-1 was superior to all the triazoles viz. Flusilazole 40 EC, Hexaconazole 5 EC and Myclobutanil 10 WP, used in the study. The yield data reflected a similar trend wherein the maximum percent increase in yield was observed in case of Polyoxin D Zinc salt 5% SC @ 600 ml ha-1 i.e. 57.47 as compared to untreated control. However, all the triazoles manifested a higher percent increase in yield as compared to the lowest dose of the test fungicide i.e. 200 ml ha-1. For the detection and quantification of polyoxin D residue in grape, we have developed an efficient and effective analytical method, using liquid chromatography-tandem mass spectrometry (LC-MS/MS), in field treated samples. The residue data had excellent fit to 1st+1st order models giving r2 value of >0.99 with a half-life (t1/2) 8.0 days for recommended dose and 14.5 days for double dose. These findings are useful for effective disease management in grape crop amalgamated with food safety and consumer satisfaction.

Modelling the Impacts of Climate Change on the Yield of Crops

This study aims to improve the understanding of the impact changes being experienced in our climate system will have on the level of crop productivity in the immediate period as well as in the nearest future. Nigeria was used as a case study and an observed climatic dataset was obtained and used alongside collected 20 year cassava, rice and soybean yield data to develop models that were applied to estimate future crop yield. Four statistically downscaled and bias-corrected Global Climate Models (GCMs): NOAA, MIROC5, ICHEC, and NCC performed simulations for the period 1985–2100 under the Representative Concentration Pathway RCP8.5. These were used to predict how the yields of cassava, rice and soybean will be in the years 2020-2050 and 2070-2100 for the 36 states in Nigeria and the FCT. 89 Empirical models were developed to estimate the yields of the three crops earlier mentioned across Nigeria with their coefficient of determination (R2) ranging between 15% - 99%. The result showed an increase of 3.91% (P<0.001), 0.08, 1.79 (P<0.1) and a decrease of 0.93% for cassava yield for ICHEC, MIROC, NOAA and NCC respectively. It also projected an increase in yield of 8.88% (P<0.001), 7.77% (P<0.001), 6.62% (P<0.001) and 8.85% (P<0.001) for Rice yield using climatic data from ICHEC, MIROC, NOAA and NCC respectively. Soybean, increase in yield are 2.81% (P<0.01), 5.84% (P<0.001), 11.38 (P<0.001) and 9.06% (P<0.001) for ICHEC, MIROC, NOAA and NCC respectively.

Climate Change Influence On Ontario Corn Farms’ Income.

Our study quantifies the impact of climate change on the income of corn farms in Ontario, at the 2068 horizon, under several warming scenarios. It is articulated around a discrete- time dynamic model of corn farm income with an annual time-step, corresponding to one agricultural cycle from planting to harvest. At each period, we compute the income of a farm given the corn yield, which is highly dependent on weather variables: temperature and rainfall. We also provide a reproducible forecast of the yearly distribution of corn yield for the regions around ten cities in Ontario, located where most of the corn growing activity takes place in the province. The price of corn futures at harvest time is taken into account and we fit our model by using 49 years of county-level historical climate and corn yield data. We then conduct out-of-sample Monte-Carlo simulations in order to obtain the farm income forecasts under a given climate change scenario, from 0 ° C to + 4 ° C.