Agro-climatic classification systems for estimating the global distribution of livestock numbers and commodities

Global insight into understanding wheat yield and production through Agro-Ecological Zoning

Global food security requires food production to be increased in the future decades. Agro-Ecological Zones (AEZ) methodology is a successful approach used in land evaluation studies to support sustainable agricultural development. This approach can facilitate finding suitable areas for wheat production on a global scale. This study was conducted based on a climate zone map, soil data, wheat cultivation area, yield, and production under irrigated and rainfed conditions, worldwide. The results for irrigated wheat indicated that there is an area of 59.5 Mha with an average yield of 4.02 t/ha which leads to the production of about 239.6 Mt of grain yield. Furthermore, climate zones (CZs) of 8002, 5203, 5302, 5403, and 8102 have the highest wheat production with an average of 20.7, 14.2, 13.3, 11.8, 11.5 Mt, respectively. The highest amount of irrigated wheat production has been achieved in soil type code 17 (Loam LF120), which has a cultivation area of around 23.6 Mha and a production of about 106.8 Mt. Rainfed wheat production is 410 Mt, and the cultivation area is 160.2 Mha. The highest rainfed wheat production with an average of 17 Mt was related to the 3702 CZ, followed by the other CZs (3802, 1303, 1203, 3602, 4602, etc.). The soil codes 11 (Loam HF120), 10 (Loam HF180), and 14 (Loam MF120) showed the highest rainfed wheat production. The findings of this study can be useful for agricultural scientists, authorities, and decision-makers around the world to find suitable lands to expand wheat cultivation and also to find new locations for increasing global wheat production to feed the increasing population in the world.

Improving Quantitative Rainfall Prediction Using Ensemble Analogues in the Tropics: Case Study of Uganda

Accurate and timely rainfall prediction enhances productivity and can aid proper planning in sectors such as agriculture, health, transport and water resources. However quantitative rainfall prediction is normally a challenge and for this reason, this study was conducted with an aim of improving rainfall prediction using ensemble methods. It first assessed the performance of six convective schemes (Kain–Fritsch (KF); Betts–Miller–Janjić (BMJ); Grell–Fretas (GF); Grell 3D ensemble (G3); New–Tiedke (NT) and Grell–Devenyi (GD)) using the root mean square error (RMSE) and mean error (ME) focusing on the March–May 2013 rainfall period over Uganda. 18 ensemble members were then generated from the three best performing convective schemes (i.e., KF, GF and G3). The daily rainfall predicted by the three ensemble methods (i.e., ensemble mean (ENS); ensemble mean analogue (EMA) and multi–member analogue ensemble (MAEM)) was then compared with the observed daily rainfall and the RMSE and ME computed. The results shows that the ENS presented a smaller RMSE compared to individual schemes (ENS: 10.02; KF: 23.96; BMJ: 26.04; GF: 25.85; G3: 24.07; NT: 29.13 and GD: 26.27) and a better bias (ENS: −1.28; KF: −1.62; BMJ: −4.04; GF: −3.90; G3: −3.62; NT: −5.41 and GD: −4.07). The EMA and MAEM presented 13 out of 21 stations and 17 out of 21 stations respectively with smaller RMSE compared to ENS thus demonstrating additional improvement in predictive performance. This study proposed and described MAEM and found it producing comparatively better quantitative rainfall prediction performance compared to the other ensemble methods used. The MAEM method should be valid regardless the nature of the rainfall season.

Species distribution models: A comparison of statistical approaches for livestock and disease epidemics

In livestock industries, reliable up-to-date spatial distribution and abundance records for animals and farms are critical for governments to manage and respond to risks. Yet few, if any, countries can afford to maintain comprehensive, up-to-date agricultural census data. Statistical modelling can be used as a proxy for such data but comparative modelling studies have rarely been undertaken for livestock populations. Widespread species, including livestock, can be difficult to model effectively due to complex spatial distributions that do not respond predictably to environmental gradients. We assessed three machine learning species distribution models (SDM) for their capacity to estimate national-level farm animal population numbers within property boundaries: boosted regression trees (BRT), random forests (RF) and K-nearest neighbour (K-NN). The models were built from a commercial livestock database and environmental and socio-economic predictor data for New Zealand. We used two spatial data stratifications to test (i) support for decision making in an emergency response situation, and (ii) the ability for the models to predict to new geographic regions. The performance of the three model types varied substantially, but the best performing models showed very high accuracy. BRTs had the best performance overall, but RF performed equally well or better in many simulations; RFs were superior at predicting livestock numbers for all but very large commercial farms. K-NN performed poorly relative to both RF and BRT in all simulations. The predictions of both multi species and single species models for farms and within hypothetical quarantine zones were very close to observed data. These models are generally applicable for livestock estimation with broad applications in disease risk modelling, biosecurity, policy and planning.